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Best Big Data Analytics Software

Matthew Miller
MM
Researched and written by Matthew Miller

Big data analytics software provides insights into large data sets that are collected from big data clusters. These tools help business users digest data trends, patterns, and anomalies and synthesize the information into understandable data visualizations, reports, and dashboards. Because of the unstructured nature of big data clusters, these analytics solutions often require a query language to pull the data out of the file system. Some solutions may offer self-service features so that non-technical employees can assemble their own charts and graphs from big data sets.

Some big data analytics solutions offer features powered by machine learning, such as natural language processing, allowing the user to query company data in a natural manner. Big data analytics software is commonly used at companies running Hadoop in conjunction with big data processing and distribution software to collect and store data. In addition, these products typically integrate with data warehouse software, the central storage hub for a company’s integrated data.

Big data analytics software differs from analytics platforms inasmuch as the former are solely focused on the manipulation of complex and large scale big data clusters into understandable visualizations, while the latter are geared toward a wide range of data sources and connectors. The two categories are mutually exclusive, and those products which are solely focused on big data use cases are only categorized in the big data analytics category.

To qualify for inclusion in the Big Data Analytics category, a product must:

Consume data, query file systems, and connect directly to big data clusters
Allow users to prepare complex big data sets into helpful and understandable data visualizations
Create business-applicable reports, visualizations, and dashboards based on discoveries inside the data sets

Best Big Data Analytics Software At A Glance

Best for Small Businesses:
Best for Mid-Market:
Best for Enterprise:
Highest User Satisfaction:
Best Free Software:
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Best for Enterprise:
Highest User Satisfaction:
Best Free Software:

G2 takes pride in showing unbiased reviews on user satisfaction in our ratings and reports. We do not allow paid placements in any of our ratings, rankings, or reports. Learn about our scoring methodologies.

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86 Listings in Big Data Analytics Available
(1,090)4.5 out of 5
3rd Easiest To Use in Big Data Analytics software
View top Consulting Services for Google Cloud BigQuery
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Entry Level Price:Free
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    BigQuery is a fully managed, AI-ready data analytics platform that helps you maximize value from your data and is designed to be multi-engine, multi-format, and multi-cloud. Store 10 GiB of data and

    Users
    • Data Engineer
    • Data Analyst
    Industries
    • Information Technology and Services
    • Computer Software
    Market Segment
    • 38% Enterprise
    • 33% Mid-Market
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Google Cloud BigQuery Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    344
    Speed
    191
    Fast Querying
    171
    Querying
    162
    Performance
    160
    Cons
    Expensive
    153
    Query Issues
    139
    Learning Curve
    109
    Cost Issues
    86
    Cost Management
    84
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Google Cloud BigQuery features and usability ratings that predict user satisfaction
    8.7
    Has the product been a good partner in doing business?
    Average: 8.8
    8.7
    Multi-Source Analysis
    Average: 8.4
    8.8
    Real-Time Analytics
    Average: 8.4
    8.7
    Data Workflow
    Average: 8.3
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Google
    Company Website
    Year Founded
    1998
    HQ Location
    Mountain View, CA
    Twitter
    @google
    32,520,271 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    301,875 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

BigQuery is a fully managed, AI-ready data analytics platform that helps you maximize value from your data and is designed to be multi-engine, multi-format, and multi-cloud. Store 10 GiB of data and

Users
  • Data Engineer
  • Data Analyst
Industries
  • Information Technology and Services
  • Computer Software
Market Segment
  • 38% Enterprise
  • 33% Mid-Market
Google Cloud BigQuery Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
344
Speed
191
Fast Querying
171
Querying
162
Performance
160
Cons
Expensive
153
Query Issues
139
Learning Curve
109
Cost Issues
86
Cost Management
84
Google Cloud BigQuery features and usability ratings that predict user satisfaction
8.7
Has the product been a good partner in doing business?
Average: 8.8
8.7
Multi-Source Analysis
Average: 8.4
8.8
Real-Time Analytics
Average: 8.4
8.7
Data Workflow
Average: 8.3
Seller Details
Seller
Google
Company Website
Year Founded
1998
HQ Location
Mountain View, CA
Twitter
@google
32,520,271 Twitter followers
LinkedIn® Page
www.linkedin.com
301,875 employees on LinkedIn®
(403)4.6 out of 5
Optimized for quick response
5th Easiest To Use in Big Data Analytics software
View top Consulting Services for Databricks Data Intelligence Platform
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  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Databricks is the Data and AI company. More than 10,000 organizations worldwide — including Block, Comcast, Conde Nast, Rivian, and Shell, and over 60% of the Fortune 500 — rely on the Databricks Data

    Users
    • Data Engineer
    • Data Scientist
    Industries
    • Information Technology and Services
    • Computer Software
    Market Segment
    • 47% Enterprise
    • 34% Mid-Market
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Databricks Data Intelligence Platform Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    182
    Features
    165
    Integrations
    104
    Data Management
    91
    Easy Integrations
    88
    Cons
    Learning Curve
    55
    Missing Features
    52
    Steep Learning Curve
    52
    Expensive
    49
    Performance Issues
    36
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Databricks Data Intelligence Platform features and usability ratings that predict user satisfaction
    8.5
    Has the product been a good partner in doing business?
    Average: 8.8
    8.9
    Multi-Source Analysis
    Average: 8.4
    8.6
    Real-Time Analytics
    Average: 8.4
    8.7
    Data Workflow
    Average: 8.3
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Company Website
    Year Founded
    1999
    HQ Location
    San Francisco, CA
    Twitter
    @databricks
    75,952 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    9,769 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Databricks is the Data and AI company. More than 10,000 organizations worldwide — including Block, Comcast, Conde Nast, Rivian, and Shell, and over 60% of the Fortune 500 — rely on the Databricks Data

Users
  • Data Engineer
  • Data Scientist
Industries
  • Information Technology and Services
  • Computer Software
Market Segment
  • 47% Enterprise
  • 34% Mid-Market
Databricks Data Intelligence Platform Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
182
Features
165
Integrations
104
Data Management
91
Easy Integrations
88
Cons
Learning Curve
55
Missing Features
52
Steep Learning Curve
52
Expensive
49
Performance Issues
36
Databricks Data Intelligence Platform features and usability ratings that predict user satisfaction
8.5
Has the product been a good partner in doing business?
Average: 8.8
8.9
Multi-Source Analysis
Average: 8.4
8.6
Real-Time Analytics
Average: 8.4
8.7
Data Workflow
Average: 8.3
Seller Details
Company Website
Year Founded
1999
HQ Location
San Francisco, CA
Twitter
@databricks
75,952 Twitter followers
LinkedIn® Page
www.linkedin.com
9,769 employees on LinkedIn®

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(584)4.5 out of 5
Optimized for quick response
1st Easiest To Use in Big Data Analytics software
View top Consulting Services for Snowflake
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Entry Level Price:$2 Compute/Hour
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Snowflake makes enterprise AI easy, efficient and trusted. Thousands of companies around the globe, including hundreds of the world’s largest, use Snowflake’s AI Data Cloud to share data, build applic

    Users
    • Data Engineer
    • Software Engineer
    Industries
    • Computer Software
    • Information Technology and Services
    Market Segment
    • 47% Enterprise
    • 40% Mid-Market
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Snowflake Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    131
    Features
    75
    Data Management
    63
    Efficiency Improvement
    61
    Database Management
    58
    Cons
    Expensive
    55
    Feature Limitations
    46
    Limited Features
    33
    Missing Features
    28
    Query Issues
    28
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Snowflake features and usability ratings that predict user satisfaction
    9.0
    Has the product been a good partner in doing business?
    Average: 8.8
    8.8
    Multi-Source Analysis
    Average: 8.4
    9.1
    Real-Time Analytics
    Average: 8.4
    8.8
    Data Workflow
    Average: 8.3
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Company Website
    Year Founded
    2012
    HQ Location
    San Mateo, CA
    Twitter
    @SnowflakeDB
    55,795 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    8,874 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Snowflake makes enterprise AI easy, efficient and trusted. Thousands of companies around the globe, including hundreds of the world’s largest, use Snowflake’s AI Data Cloud to share data, build applic

Users
  • Data Engineer
  • Software Engineer
Industries
  • Computer Software
  • Information Technology and Services
Market Segment
  • 47% Enterprise
  • 40% Mid-Market
Snowflake Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
131
Features
75
Data Management
63
Efficiency Improvement
61
Database Management
58
Cons
Expensive
55
Feature Limitations
46
Limited Features
33
Missing Features
28
Query Issues
28
Snowflake features and usability ratings that predict user satisfaction
9.0
Has the product been a good partner in doing business?
Average: 8.8
8.8
Multi-Source Analysis
Average: 8.4
9.1
Real-Time Analytics
Average: 8.4
8.8
Data Workflow
Average: 8.3
Seller Details
Company Website
Year Founded
2012
HQ Location
San Mateo, CA
Twitter
@SnowflakeDB
55,795 Twitter followers
LinkedIn® Page
www.linkedin.com
8,874 employees on LinkedIn®
(216)4.5 out of 5
4th Easiest To Use in Big Data Analytics software
Save to My Lists
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Accelerate innovation by enabling data science with a high-performance analytics platform that's optimized for Azure.

    Users
    • Software Engineer
    • Data Engineer
    Industries
    • Information Technology and Services
    • Computer Software
    Market Segment
    • 50% Enterprise
    • 26% Small-Business
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Azure Databricks Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    28
    Features
    13
    Performance
    12
    Integrations
    11
    Analytics
    9
    Cons
    Expensive
    10
    Learning Curve
    6
    Poor UI Design
    6
    Poor Customer Support
    4
    Slow Performance
    4
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Azure Databricks features and usability ratings that predict user satisfaction
    8.8
    Has the product been a good partner in doing business?
    Average: 8.8
    9.0
    Multi-Source Analysis
    Average: 8.4
    8.8
    Real-Time Analytics
    Average: 8.4
    8.6
    Data Workflow
    Average: 8.3
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Microsoft
    Year Founded
    1975
    HQ Location
    Redmond, Washington
    Twitter
    @microsoft
    14,031,499 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    238,990 employees on LinkedIn®
    Ownership
    MSFT
Product Description
How are these determined?Information
This description is provided by the seller.

Accelerate innovation by enabling data science with a high-performance analytics platform that's optimized for Azure.

Users
  • Software Engineer
  • Data Engineer
Industries
  • Information Technology and Services
  • Computer Software
Market Segment
  • 50% Enterprise
  • 26% Small-Business
Azure Databricks Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
28
Features
13
Performance
12
Integrations
11
Analytics
9
Cons
Expensive
10
Learning Curve
6
Poor UI Design
6
Poor Customer Support
4
Slow Performance
4
Azure Databricks features and usability ratings that predict user satisfaction
8.8
Has the product been a good partner in doing business?
Average: 8.8
9.0
Multi-Source Analysis
Average: 8.4
8.8
Real-Time Analytics
Average: 8.4
8.6
Data Workflow
Average: 8.3
Seller Details
Seller
Microsoft
Year Founded
1975
HQ Location
Redmond, Washington
Twitter
@microsoft
14,031,499 Twitter followers
LinkedIn® Page
www.linkedin.com
238,990 employees on LinkedIn®
Ownership
MSFT
(625)4.6 out of 5
Optimized for quick response
2nd Easiest To Use in Big Data Analytics software
View top Consulting Services for Alteryx
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  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    The Alteryx AI Platform for Enterprise Analytics offers integrated generative and conversational AI, data preparation, advanced analytics, and automated reporting capabilities. The platform is powered

    Users
    • Data Analyst
    • Consultant
    Industries
    • Financial Services
    • Accounting
    Market Segment
    • 64% Enterprise
    • 22% Mid-Market
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Alteryx Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    146
    Intuitive
    55
    Automation
    53
    Easy Learning
    46
    Ease of Learning
    43
    Cons
    Learning Curve
    40
    Expensive
    34
    Learning Difficulty
    25
    Complexity
    19
    Missing Features
    17
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Alteryx features and usability ratings that predict user satisfaction
    9.0
    Has the product been a good partner in doing business?
    Average: 8.8
    9.0
    Multi-Source Analysis
    Average: 8.4
    8.6
    Real-Time Analytics
    Average: 8.4
    9.2
    Data Workflow
    Average: 8.3
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Alteryx
    Company Website
    Year Founded
    1997
    HQ Location
    Irvine, CA
    Twitter
    @alteryx
    26,732 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    2,287 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

The Alteryx AI Platform for Enterprise Analytics offers integrated generative and conversational AI, data preparation, advanced analytics, and automated reporting capabilities. The platform is powered

Users
  • Data Analyst
  • Consultant
Industries
  • Financial Services
  • Accounting
Market Segment
  • 64% Enterprise
  • 22% Mid-Market
Alteryx Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
146
Intuitive
55
Automation
53
Easy Learning
46
Ease of Learning
43
Cons
Learning Curve
40
Expensive
34
Learning Difficulty
25
Complexity
19
Missing Features
17
Alteryx features and usability ratings that predict user satisfaction
9.0
Has the product been a good partner in doing business?
Average: 8.8
9.0
Multi-Source Analysis
Average: 8.4
8.6
Real-Time Analytics
Average: 8.4
9.2
Data Workflow
Average: 8.3
Seller Details
Seller
Alteryx
Company Website
Year Founded
1997
HQ Location
Irvine, CA
Twitter
@alteryx
26,732 Twitter followers
LinkedIn® Page
www.linkedin.com
2,287 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Azure Synapse Analytics is a cloud-based Enterprise Data Warehouse (EDW) that leverages Massively Parallel Processing (MPP) to quickly run complex queries across petabytes of data.

    Users
    No information available
    Industries
    • Information Technology and Services
    Market Segment
    • 34% Mid-Market
    • 29% Enterprise
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Azure Synapse Analytics Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Analytics
    2
    Data Security
    2
    Performance
    2
    Scalability
    2
    Security
    2
    Cons
    Data Management
    1
    Feature Limitations
    1
    Importing Issues
    1
    Integration Issues
    1
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Azure Synapse Analytics features and usability ratings that predict user satisfaction
    8.8
    Has the product been a good partner in doing business?
    Average: 8.8
    8.8
    Multi-Source Analysis
    Average: 8.4
    8.9
    Real-Time Analytics
    Average: 8.4
    9.0
    Data Workflow
    Average: 8.3
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Microsoft
    Year Founded
    1975
    HQ Location
    Redmond, Washington
    Twitter
    @microsoft
    14,031,499 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    238,990 employees on LinkedIn®
    Ownership
    MSFT
Product Description
How are these determined?Information
This description is provided by the seller.

Azure Synapse Analytics is a cloud-based Enterprise Data Warehouse (EDW) that leverages Massively Parallel Processing (MPP) to quickly run complex queries across petabytes of data.

Users
No information available
Industries
  • Information Technology and Services
Market Segment
  • 34% Mid-Market
  • 29% Enterprise
Azure Synapse Analytics Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Analytics
2
Data Security
2
Performance
2
Scalability
2
Security
2
Cons
Data Management
1
Feature Limitations
1
Importing Issues
1
Integration Issues
1
Azure Synapse Analytics features and usability ratings that predict user satisfaction
8.8
Has the product been a good partner in doing business?
Average: 8.8
8.8
Multi-Source Analysis
Average: 8.4
8.9
Real-Time Analytics
Average: 8.4
9.0
Data Workflow
Average: 8.3
Seller Details
Seller
Microsoft
Year Founded
1975
HQ Location
Redmond, Washington
Twitter
@microsoft
14,031,499 Twitter followers
LinkedIn® Page
www.linkedin.com
238,990 employees on LinkedIn®
Ownership
MSFT
(79)4.4 out of 5
Optimized for quick response
8th Easiest To Use in Big Data Analytics software
View top Consulting Services for Starburst
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  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Starburst offers the fastest and most scalable data lakehouse, built on enhanced Trino, a leading open-source MPP SQL engine. This high-performance architecture enables businesses to increase the valu

    Users
    No information available
    Industries
    • Information Technology and Services
    • Banking
    Market Segment
    • 48% Enterprise
    • 28% Small-Business
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Starburst Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    26
    Fast Querying
    19
    Integrations
    18
    Query Efficiency
    18
    Performance
    17
    Cons
    Slow Performance
    12
    Difficult Setup
    11
    Difficulty
    10
    Learning Curve
    10
    Query Issues
    10
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Starburst features and usability ratings that predict user satisfaction
    9.0
    Has the product been a good partner in doing business?
    Average: 8.8
    8.7
    Multi-Source Analysis
    Average: 8.4
    8.0
    Real-Time Analytics
    Average: 8.4
    7.9
    Data Workflow
    Average: 8.3
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Starburst
    Company Website
    Year Founded
    2017
    HQ Location
    Boston, MA
    Twitter
    @starburstdata
    3,421 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    510 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Starburst offers the fastest and most scalable data lakehouse, built on enhanced Trino, a leading open-source MPP SQL engine. This high-performance architecture enables businesses to increase the valu

Users
No information available
Industries
  • Information Technology and Services
  • Banking
Market Segment
  • 48% Enterprise
  • 28% Small-Business
Starburst Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
26
Fast Querying
19
Integrations
18
Query Efficiency
18
Performance
17
Cons
Slow Performance
12
Difficult Setup
11
Difficulty
10
Learning Curve
10
Query Issues
10
Starburst features and usability ratings that predict user satisfaction
9.0
Has the product been a good partner in doing business?
Average: 8.8
8.7
Multi-Source Analysis
Average: 8.4
8.0
Real-Time Analytics
Average: 8.4
7.9
Data Workflow
Average: 8.3
Seller Details
Seller
Starburst
Company Website
Year Founded
2017
HQ Location
Boston, MA
Twitter
@starburstdata
3,421 Twitter followers
LinkedIn® Page
www.linkedin.com
510 employees on LinkedIn®
(324)4.3 out of 5
9th Easiest To Use in Big Data Analytics software
Save to My Lists
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    At Teradata, we believe that people thrive when empowered with better information. That’s why we built the most complete cloud analytics and data platform for AI. By delivering harmonized data, trust

    Users
    • Software Engineer
    • Data Engineer
    Industries
    • Information Technology and Services
    • Financial Services
    Market Segment
    • 71% Enterprise
    • 19% Mid-Market
    User Sentiment
    How are these determined?Information
    These insights, currently in beta, are compiled from user reviews and grouped to display a high-level overview of the software.
    • PrestoSQL, now replaced by Trino, is a product that offers connectivity to a variety of data storage platforms and expands the number of external data sources that Teradata users can explore.
    • Users like the product's language integration, ease of use, deployment flexibility, and the support provided by the Teradata team during implementation and ongoing support afterwards.
    • Reviewers mentioned that the compatibility of Teradata QueryGrid and their supporting PrestoSQL instance falls behind the open-source community, and integrations often have to remain several versions behind what is available elsewhere.
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Teradata Vantage Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    58
    Performance
    38
    Features
    35
    Analytics
    32
    Scalability
    32
    Cons
    Expensive
    23
    Learning Curve
    15
    Complexity
    13
    Data Management Issues
    13
    Missing Features
    12
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Teradata Vantage features and usability ratings that predict user satisfaction
    8.2
    Has the product been a good partner in doing business?
    Average: 8.8
    8.0
    Multi-Source Analysis
    Average: 8.4
    8.1
    Real-Time Analytics
    Average: 8.4
    8.0
    Data Workflow
    Average: 8.3
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Teradata
    Company Website
    Year Founded
    1979
    HQ Location
    San Diego, CA
    Twitter
    @Teradata
    88,712 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    10,355 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

At Teradata, we believe that people thrive when empowered with better information. That’s why we built the most complete cloud analytics and data platform for AI. By delivering harmonized data, trust

Users
  • Software Engineer
  • Data Engineer
Industries
  • Information Technology and Services
  • Financial Services
Market Segment
  • 71% Enterprise
  • 19% Mid-Market
User Sentiment
How are these determined?Information
These insights, currently in beta, are compiled from user reviews and grouped to display a high-level overview of the software.
  • PrestoSQL, now replaced by Trino, is a product that offers connectivity to a variety of data storage platforms and expands the number of external data sources that Teradata users can explore.
  • Users like the product's language integration, ease of use, deployment flexibility, and the support provided by the Teradata team during implementation and ongoing support afterwards.
  • Reviewers mentioned that the compatibility of Teradata QueryGrid and their supporting PrestoSQL instance falls behind the open-source community, and integrations often have to remain several versions behind what is available elsewhere.
Teradata Vantage Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
58
Performance
38
Features
35
Analytics
32
Scalability
32
Cons
Expensive
23
Learning Curve
15
Complexity
13
Data Management Issues
13
Missing Features
12
Teradata Vantage features and usability ratings that predict user satisfaction
8.2
Has the product been a good partner in doing business?
Average: 8.8
8.0
Multi-Source Analysis
Average: 8.4
8.1
Real-Time Analytics
Average: 8.4
8.0
Data Workflow
Average: 8.3
Seller Details
Seller
Teradata
Company Website
Year Founded
1979
HQ Location
San Diego, CA
Twitter
@Teradata
88,712 Twitter followers
LinkedIn® Page
www.linkedin.com
10,355 employees on LinkedIn®
(37)4.2 out of 5
View top Consulting Services for Azure Data Lake Analytics
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  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Azure Data Lake Analytics is a distributed, cloud-based data processing architecture offered by Microsoft in the Azure cloud. It is based on YARN, the same as the open-source Hadoop platform.

    Users
    No information available
    Industries
    • Information Technology and Services
    • Computer Software
    Market Segment
    • 54% Enterprise
    • 27% Mid-Market
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Azure Data Lake Analytics features and usability ratings that predict user satisfaction
    8.6
    Has the product been a good partner in doing business?
    Average: 8.8
    7.9
    Multi-Source Analysis
    Average: 8.4
    8.1
    Real-Time Analytics
    Average: 8.4
    8.5
    Data Workflow
    Average: 8.3
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Microsoft
    Year Founded
    1975
    HQ Location
    Redmond, Washington
    Twitter
    @microsoft
    14,031,499 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    238,990 employees on LinkedIn®
    Ownership
    MSFT
Product Description
How are these determined?Information
This description is provided by the seller.

Azure Data Lake Analytics is a distributed, cloud-based data processing architecture offered by Microsoft in the Azure cloud. It is based on YARN, the same as the open-source Hadoop platform.

Users
No information available
Industries
  • Information Technology and Services
  • Computer Software
Market Segment
  • 54% Enterprise
  • 27% Mid-Market
Azure Data Lake Analytics features and usability ratings that predict user satisfaction
8.6
Has the product been a good partner in doing business?
Average: 8.8
7.9
Multi-Source Analysis
Average: 8.4
8.1
Real-Time Analytics
Average: 8.4
8.5
Data Workflow
Average: 8.3
Seller Details
Seller
Microsoft
Year Founded
1975
HQ Location
Redmond, Washington
Twitter
@microsoft
14,031,499 Twitter followers
LinkedIn® Page
www.linkedin.com
238,990 employees on LinkedIn®
Ownership
MSFT
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    MATLAB is a programming, modeling and simulation tool developed by MathWorks.

    Users
    • Graduate Research Assistant
    • Student
    Industries
    • Higher Education
    • Research
    Market Segment
    • 43% Enterprise
    • 30% Small-Business
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • MATLAB Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    9
    Simulation
    6
    Features
    5
    Problem Solving
    5
    Insights
    4
    Cons
    Expensive
    3
    Cost
    2
    Difficult Learning
    2
    High System Requirements
    2
    Lack of Features
    2
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • MATLAB features and usability ratings that predict user satisfaction
    8.4
    Has the product been a good partner in doing business?
    Average: 8.8
    8.4
    Multi-Source Analysis
    Average: 8.4
    8.7
    Real-Time Analytics
    Average: 8.4
    8.9
    Data Workflow
    Average: 8.3
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    MathWorks
    Year Founded
    1984
    HQ Location
    Natick, MA
    Twitter
    @MATLAB
    99,670 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    7,496 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

MATLAB is a programming, modeling and simulation tool developed by MathWorks.

Users
  • Graduate Research Assistant
  • Student
Industries
  • Higher Education
  • Research
Market Segment
  • 43% Enterprise
  • 30% Small-Business
MATLAB Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
9
Simulation
6
Features
5
Problem Solving
5
Insights
4
Cons
Expensive
3
Cost
2
Difficult Learning
2
High System Requirements
2
Lack of Features
2
MATLAB features and usability ratings that predict user satisfaction
8.4
Has the product been a good partner in doing business?
Average: 8.8
8.4
Multi-Source Analysis
Average: 8.4
8.7
Real-Time Analytics
Average: 8.4
8.9
Data Workflow
Average: 8.3
Seller Details
Seller
MathWorks
Year Founded
1984
HQ Location
Natick, MA
Twitter
@MATLAB
99,670 Twitter followers
LinkedIn® Page
www.linkedin.com
7,496 employees on LinkedIn®
By IBM
(90)4.3 out of 5
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  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    IBM Cloud Pak® for Data is a fully integrated data and AI platform that modernizes how businesses collect, organize and analyze data, forming the foundation to infuse AI across their organization. Run

    Users
    No information available
    Industries
    • Computer Software
    • Information Technology and Services
    Market Segment
    • 50% Enterprise
    • 28% Small-Business
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • IBM Cloud Pak for Data Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Features
    10
    Ease of Use
    8
    Insights
    8
    Analytics
    6
    Data Management
    6
    Cons
    Complexity
    6
    Learning Curve
    6
    Poor User Interface
    5
    Complexity Issues
    4
    Expensive
    4
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • IBM Cloud Pak for Data features and usability ratings that predict user satisfaction
    8.1
    Has the product been a good partner in doing business?
    Average: 8.8
    8.1
    Multi-Source Analysis
    Average: 8.4
    8.5
    Real-Time Analytics
    Average: 8.4
    8.9
    Data Workflow
    Average: 8.3
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    IBM
    Year Founded
    1911
    HQ Location
    Armonk, NY
    Twitter
    @IBM
    711,154 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    317,108 employees on LinkedIn®
    Ownership
    SWX:IBM
Product Description
How are these determined?Information
This description is provided by the seller.

IBM Cloud Pak® for Data is a fully integrated data and AI platform that modernizes how businesses collect, organize and analyze data, forming the foundation to infuse AI across their organization. Run

Users
No information available
Industries
  • Computer Software
  • Information Technology and Services
Market Segment
  • 50% Enterprise
  • 28% Small-Business
IBM Cloud Pak for Data Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Features
10
Ease of Use
8
Insights
8
Analytics
6
Data Management
6
Cons
Complexity
6
Learning Curve
6
Poor User Interface
5
Complexity Issues
4
Expensive
4
IBM Cloud Pak for Data features and usability ratings that predict user satisfaction
8.1
Has the product been a good partner in doing business?
Average: 8.8
8.1
Multi-Source Analysis
Average: 8.4
8.5
Real-Time Analytics
Average: 8.4
8.9
Data Workflow
Average: 8.3
Seller Details
Seller
IBM
Year Founded
1911
HQ Location
Armonk, NY
Twitter
@IBM
711,154 Twitter followers
LinkedIn® Page
www.linkedin.com
317,108 employees on LinkedIn®
Ownership
SWX:IBM
(64)4.6 out of 5
Optimized for quick response
10th Easiest To Use in Big Data Analytics software
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  • Overview
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Dremio is the unified lakehouse platform for self-service analytics and AI, serving hundreds of global enterprises, including Maersk, Amazon, Regeneron, NetApp, and S&P Global. Customers rely on D

    Users
    No information available
    Industries
    • Financial Services
    • Information Technology and Services
    Market Segment
    • 50% Enterprise
    • 44% Mid-Market
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Dremio Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    13
    Integrations
    10
    Performance
    7
    Large Datasets
    6
    SQL Support
    6
    Cons
    Difficulty
    5
    Poor Customer Support
    5
    Learning Curve
    4
    Limited Features
    3
    Technical Difficulties
    3
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Dremio features and usability ratings that predict user satisfaction
    9.2
    Has the product been a good partner in doing business?
    Average: 8.8
    9.0
    Multi-Source Analysis
    Average: 8.4
    8.3
    Real-Time Analytics
    Average: 8.4
    7.1
    Data Workflow
    Average: 8.3
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Dremio
    Company Website
    Year Founded
    2015
    HQ Location
    Santa Clara, California
    Twitter
    @dremio
    5,050 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    368 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Dremio is the unified lakehouse platform for self-service analytics and AI, serving hundreds of global enterprises, including Maersk, Amazon, Regeneron, NetApp, and S&P Global. Customers rely on D

Users
No information available
Industries
  • Financial Services
  • Information Technology and Services
Market Segment
  • 50% Enterprise
  • 44% Mid-Market
Dremio Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
13
Integrations
10
Performance
7
Large Datasets
6
SQL Support
6
Cons
Difficulty
5
Poor Customer Support
5
Learning Curve
4
Limited Features
3
Technical Difficulties
3
Dremio features and usability ratings that predict user satisfaction
9.2
Has the product been a good partner in doing business?
Average: 8.8
9.0
Multi-Source Analysis
Average: 8.4
8.3
Real-Time Analytics
Average: 8.4
7.1
Data Workflow
Average: 8.3
Seller Details
Seller
Dremio
Company Website
Year Founded
2015
HQ Location
Santa Clara, California
Twitter
@dremio
5,050 Twitter followers
LinkedIn® Page
www.linkedin.com
368 employees on LinkedIn®
(23)4.7 out of 5
7th Easiest To Use in Big Data Analytics software
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Entry Level Price:Free
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Visokio builds Omniscope Evo, complete and extensible BI software for data processing, analytics and reporting. A smart experience on any device. Start from any data in any shape, load, blend, transf

    Users
    No information available
    Industries
    • Information Technology and Services
    Market Segment
    • 65% Small-Business
    • 17% Enterprise
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Omniscope Evo Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    13
    Features
    10
    Analytics
    9
    Automation
    7
    Usability
    7
    Cons
    Missing Features
    4
    Dashboard Issues
    2
    Expensive
    2
    Limited Customization
    2
    Limited Visualization
    2
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Omniscope Evo features and usability ratings that predict user satisfaction
    9.5
    Has the product been a good partner in doing business?
    Average: 8.8
    8.9
    Multi-Source Analysis
    Average: 8.4
    8.4
    Real-Time Analytics
    Average: 8.4
    9.8
    Data Workflow
    Average: 8.3
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Year Founded
    2002
    HQ Location
    London, GB
    Twitter
    @Visokio
    263 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    8 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Visokio builds Omniscope Evo, complete and extensible BI software for data processing, analytics and reporting. A smart experience on any device. Start from any data in any shape, load, blend, transf

Users
No information available
Industries
  • Information Technology and Services
Market Segment
  • 65% Small-Business
  • 17% Enterprise
Omniscope Evo Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
13
Features
10
Analytics
9
Automation
7
Usability
7
Cons
Missing Features
4
Dashboard Issues
2
Expensive
2
Limited Customization
2
Limited Visualization
2
Omniscope Evo features and usability ratings that predict user satisfaction
9.5
Has the product been a good partner in doing business?
Average: 8.8
8.9
Multi-Source Analysis
Average: 8.4
8.4
Real-Time Analytics
Average: 8.4
9.8
Data Workflow
Average: 8.3
Seller Details
Year Founded
2002
HQ Location
London, GB
Twitter
@Visokio
263 Twitter followers
LinkedIn® Page
www.linkedin.com
8 employees on LinkedIn®
(159)4.8 out of 5
6th Easiest To Use in Big Data Analytics software
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  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    dbt is a transformation workflow that lets data teams quickly and collaboratively deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documenta

    Users
    • Analytics Engineer
    • Data Engineer
    Industries
    • Computer Software
    • Information Technology and Services
    Market Segment
    • 59% Mid-Market
    • 25% Small-Business
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • dbt Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    30
    Features
    23
    Transformation
    16
    Integrations
    13
    Analytics
    12
    Cons
    Feature Limitations
    13
    Missing Features
    12
    Limited Functionality
    10
    Learning Curve
    7
    Learning Difficulty
    7
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • dbt features and usability ratings that predict user satisfaction
    8.8
    Has the product been a good partner in doing business?
    Average: 8.8
    8.3
    Multi-Source Analysis
    Average: 8.4
    7.7
    Real-Time Analytics
    Average: 8.4
    9.5
    Data Workflow
    Average: 8.3
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    dbt Labs
    Company Website
    Year Founded
    2016
    HQ Location
    Philadelphia, US
    Twitter
    @getdbt
    13,121 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    535 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

dbt is a transformation workflow that lets data teams quickly and collaboratively deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documenta

Users
  • Analytics Engineer
  • Data Engineer
Industries
  • Computer Software
  • Information Technology and Services
Market Segment
  • 59% Mid-Market
  • 25% Small-Business
dbt Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
30
Features
23
Transformation
16
Integrations
13
Analytics
12
Cons
Feature Limitations
13
Missing Features
12
Limited Functionality
10
Learning Curve
7
Learning Difficulty
7
dbt features and usability ratings that predict user satisfaction
8.8
Has the product been a good partner in doing business?
Average: 8.8
8.3
Multi-Source Analysis
Average: 8.4
7.7
Real-Time Analytics
Average: 8.4
9.5
Data Workflow
Average: 8.3
Seller Details
Seller
dbt Labs
Company Website
Year Founded
2016
HQ Location
Philadelphia, US
Twitter
@getdbt
13,121 Twitter followers
LinkedIn® Page
www.linkedin.com
535 employees on LinkedIn®
(111)4.4 out of 5
12th Easiest To Use in Big Data Analytics software
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  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Cloud-native service for data in motion built by the original creators of Apache Kafka® Today’s consumers have the world at their fingertips and hold an unforgiving expectation for end-to-end real-ti

    Users
    • Software Engineer
    • Senior Software Engineer
    Industries
    • Computer Software
    • Information Technology and Services
    Market Segment
    • 36% Enterprise
    • 34% Small-Business
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Confluent Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    17
    Features
    12
    Easy Integrations
    11
    Scalability
    11
    Integrations
    10
    Cons
    Poor Documentation
    7
    Expensive
    6
    Limitations
    5
    Difficult Learning
    4
    Learning Curve
    4
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Confluent features and usability ratings that predict user satisfaction
    8.5
    Has the product been a good partner in doing business?
    Average: 8.8
    8.3
    Multi-Source Analysis
    Average: 8.4
    8.9
    Real-Time Analytics
    Average: 8.4
    7.9
    Data Workflow
    Average: 8.3
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Confluent
    Year Founded
    2014
    HQ Location
    Mountain View, California
    Twitter
    @ConfluentInc
    43,205 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    3,311 employees on LinkedIn®
    Ownership
    NASDAQ: CFLT
Product Description
How are these determined?Information
This description is provided by the seller.

Cloud-native service for data in motion built by the original creators of Apache Kafka® Today’s consumers have the world at their fingertips and hold an unforgiving expectation for end-to-end real-ti

Users
  • Software Engineer
  • Senior Software Engineer
Industries
  • Computer Software
  • Information Technology and Services
Market Segment
  • 36% Enterprise
  • 34% Small-Business
Confluent Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
17
Features
12
Easy Integrations
11
Scalability
11
Integrations
10
Cons
Poor Documentation
7
Expensive
6
Limitations
5
Difficult Learning
4
Learning Curve
4
Confluent features and usability ratings that predict user satisfaction
8.5
Has the product been a good partner in doing business?
Average: 8.8
8.3
Multi-Source Analysis
Average: 8.4
8.9
Real-Time Analytics
Average: 8.4
7.9
Data Workflow
Average: 8.3
Seller Details
Seller
Confluent
Year Founded
2014
HQ Location
Mountain View, California
Twitter
@ConfluentInc
43,205 Twitter followers
LinkedIn® Page
www.linkedin.com
3,311 employees on LinkedIn®
Ownership
NASDAQ: CFLT

Learn More About Big Data Analytics Software

What is Big Data Analytics Software?

The huge amount of data that is accessible to businesses today has made it a near necessity for them to implement some type of analytics software to better understand and act on that data. Implementing big data analytics software has been a major initiative for companies undergoing digital transformation, as these tools offer deeper visibility into an organization's data. Companies adopt these solutions to make sense of large data sets collected from big data clusters.

With the ability to visualize and understand business data, employees can make informed decisions. For example, retailers can use these tools to better understand inventory distribution across their channels and make data-driven decisions based on this data. Some big data analytics solutions may offer artificial intelligence or machine learning features, such as natural language processing, as an interface capability to further aid nontechnical users.

What Types of Big Data Analytics Software Exist?

Many types of big data analytics solutions share overlapping functionality, while simultaneously catering to different user personas such as data analysts and financial analysts or providing unique services.

Because of the unstructured nature of big data clusters, these analytics solutions require a query language to pull the data out of the file system. Most commercial table databases allow SQL queries; however, big data analytics tools do not necessarily offer such SQL language capabilities and may require a more intricate knowledge of querying from a data scientist. As an alternative, some solutions may offer self-service features so that the average employee can assemble their own charts and graphs from big data sets.

Self-service big data analytics tools

Self-service big data analytics tools do not require coding knowledge, so end users with limited to no coding knowledge can take advantage of them for data needs. This enables business users like sales representatives, human resource managers, marketers, and other nondata team members to make decisions based on relevant business data. Self-service solutions often provide drag-and-drop functionality for building dashboards, prebuilt templates for querying data, and, occasionally, natural language querying for data discovery. Similar to analytics platforms, organizations use these tools to build interactive dashboards for discovering actionable insights. 

Embedded analytics solutions

Embedded analytics solutions offer the ability to integrate proprietary analytics functionality within other business applications. Commonly, businesses embed analytics solutions in software such as CRMs, ERP, and portals (e.g., intranets or extranets). Businesses may choose an embedded product to promote user adoption; by placing the analytics inside regularly used software, companies enable employees to take advantage of available data. These solutions provide self-service functionality so average business end users can take advantage of data for improved decision making. 

What are the Common Features of Big Data Analytics Software?

Big data analytics software helps companies get a better understanding of their data. The following are some core features of this software: 

Data connectivity: If businesses cannot connect the requisite data, then there is no use for big data analytics software. The methods for connecting data include Hadoop and Spark integration which allows for processing and distribution workflows on top of Apache Hadoop and Apache Spark, respectively. In addition, this software should allow for analyzing data that is stored in data lakes, data warehouses, and data lake houses.

Data transformation: For data to be analyzed, it needs to be properly cleaned and transformed into a usable format. Big data analytics software provides features such as real-time analytics and data querying. With these features, businesses can gain a high-level view of their data in real time, allowing one to query it and better understand it. Through query languages like SQL, users can query their data and dig deeper into particular data sets and data points.

Data operations: Once the data is connected (or integrated) and transformed, it can be analyzed. Firstly, it is important to establish data workflows, which can help in stringing together specific functions and data sets to automate analytics iterations. In addition, big data analytics software provides the ability to visualize data through dashboards, as well as notebooks which can be used to create visualization with predefined or scheduled queries. 

It is not always the case that one will access analytics via a standalone analytics platform. Therefore, some products provide embedded analytics capabilities. This allows users to access analytics inside business applications, which allows for more streamlined work since the users need not switch between applications. 

Other Features of Big Data Analytics Software: Governed Discovery,

What are the Benefits of Big Data Analytics Software?

Data is both common and invaluable and within that data lies insights that could impact an organization's processes and performance. There are seemingly infinite insights a business can pull from their data and numerous reasons to utilize big data analytics software. 

Big data analytics software helps people make decisions easier by allowing teams to gain deeper insight into their data. With increased data literacy, teams across a business, from sales to marketing to finance can become more efficient and better understand how they can improve through data-driven initiatives. 

With big data analytics software, businesses can ingest, integrate, and prepare big data sources. Subsequently, they can connect all company data sources into a single platform to make cross-department connections, visualize and understand company data, encourage data-driven decision making for business optimization, and discover new insights that can enhance the bottom line.

Enable data-driven decision making: Businesses can use big data analytics software to fuel digital transformation by leveraging data to drive business decisions. Companies can leverage analytics and business intelligence (BI) tools to understand all aspects of the business, including hiring forecasts, which marketing campaign should be used to target certain demographics, which sales prospects to target first, supply chain optimization, and many others.

Measure and understand company performance: Organizations often leverage data visualization tools to track company key performance indicators (KPIs) in real time. From there, big data analytics software can be used to determine why the business is either exceeding or falling short of those important company metrics. When stakeholders develop a keen understanding of why the business is performing the way it is, they can make adjustments and pivots; if a team is falling short of a goal, they can examine and adjust processes as needed. It is one thing to simply know the performance of sales or web traffic numbers, but it is another to dig into the reasons behind it and adapt based on what is successful and what is not.

Discover new actionable insights: Analytics tools combine data from a variety of sources, including accounting software, enterprise resource planning (ERP) software, CRM software, marketing automation software, and others. Data analysts can leverage this integrated data to find correlations between different departments, and their processes and actions, to discover previously hidden insights. For example, it is possible that certain sales tactics have varying impacts on the numbers for one specific product versus another. 

Analysts can discover this impact by comparing the list of closed accounts from their company CRM with products shipped in their ERP system. Teams are generally siloed and use disparate software, so these insights that were traditionally more difficult to discover, are now made easier. 

Who Uses Big Data Analytics Software?

Data analysts: Depending on the complexity of the software, it is likely that analysts will be required. They can help set up the requisite queries, dashboards, and notebooks for other employees and teams. They can create complex queries inside the platforms to gather a deeper understanding of business-critical data.

Operations and supply chain teams: A company’s supply chain frequently has many touchpoints, and as a result, many data points. Therefore, employees working in operations and supply chain teams are able to use big data analytics software to gain a better understanding of their departments and the data that is generated, such as from an ERP system. These applications track everything from accounting to supply chain and distribution; by inputting supply chain data into this software, supply chain managers can optimize a number of processes to save time and resources.

Finance teams: Finance teams leverage big data analytics software to gain insight and understanding into the factors that impact an organization's bottom line. Through integrations with financial systems such as accounting software, employees such as chief financial officers (CFOs) can see how well the business is performing. As mentioned above, these employees will likely be accessing the software via self-service dashboards that were set up by data analysts. By integrating financial data with sales, marketing, and other operations data, accounting and finance teams pull actionable insights that might not have been uncovered through the use of traditional tools.

Sales and marketing teams: Sales teams also seek to improve financial metrics and can benefit tremendously from being more data-driven. Through the use of both self-service analytics tools and embedded analytics solutions, they can obtain insights into prospective accounts, sales performance, and pipeline forecasting, among many other use cases. Using analytics tools in a sales team can help businesses optimize their sales processes and influence revenue.

For marketing teams, tracking the performance of campaigns is key. Since they run different types of campaigns, including email marketing, digital advertising, or even traditional advertising campaigns, analytics tools allow marketing teams to track the performance of those campaigns in one central location.

Consultants: Businesses do not always have the luxury to build, develop, and optimize their own analytics solutions. Some businesses opt to employ external consultants, such as business intelligence (BI) consulting providers. These providers seek to understand a business and its goals, interpret data, and offer advice to ensure goals are met. BI consultants frequently have industry-specific knowledge alongside their technical backgrounds, with experience in healthcare, business, and other fields. 

What are the Alternatives to Big Data Analytics Software?

Alternatives to big data analytics software can replace this type of software, either partially or completely:

Analytics platforms: Analytics platforms might include big data integrations, but are broader-focused tools that facilitate the following five elements: data preparation, data modeling, data blending, data visualization, and insights delivery.

Log analysis software: Businesses that are focused on log data might benefit from deploying log analysis software, which is used to analyze log data from applications and systems. It should be kept in mind that this software is much more limited in terms of data types and data sources to which it can be connected to. However, since log analysis software focuses on logs, it frequently provides more granular details around log-related data.

Stream analytics software: When one is looking for tools specifically geared toward analyzing data in real time, stream analytics software is a go-to solution. These tools help users analyze data in transfer through APIs, between applications, and more. This software can be helpful with internet of things (IoT) data, which one frequently wants to analyze in real time.

Predictive analytics software: Broad-purpose big data analytics software allows businesses to conduct various forms of analysis, such as prescriptive, descriptive, and predictive. Businesses that are focused on looking at their past and present data to predict future outcomes can use predictive analytics software for a more finetuned solution. 

Text analysis software: Big data analytics software is focused on structured or numerical data, allowing users to drill down and dig into numbers to inform business decisions. If the user is looking to focus on unstructured or text data, text analysis solutions are the best bet. These tools help users quickly understand and pull sentiment analysis, key phrases, themes, and other insights from unstructured text data.

Software Related to Big Data Analytics Software

Related solutions that can be used together with big data analytics software include:

Data warehouse software: Most companies have a large number of disparate data sources, so to best integrate all their data, they implement a data warehouse. Data warehouses can house data from multiple databases and business applications, which allows BI and analytics tools to pull all company data from a single repository. This organization is critical to the quality of the data that is ingested by analytics software.

Data preparation software: A key solution necessary for easy data analysis is a data preparation tool and other related data management tools. These solutions allow users to discover, combine, clean, and enrich data for simple analysis. Data preparation tools are often used by IT teams or data analysts tasked with using BI tools. Some BI platforms offer data preparation features, but businesses with a wide range of data sources often opt for a dedicated preparation tool.

Challenges with Big Data Analytics Software

Software solutions can come with their own set of challenges. 

Need for skilled employees: Big data analytics software is not necessarily simple. Often, these tools require a dedicated administrator to help implement the solution and assist others with adoption. However, there is a shortage of skilled data scientists and analysts that are equipped to set up such solutions. Additionally, those same data scientists will be tasked with deriving actionable insights from within the data. 

Without people skilled in these areas, businesses cannot effectively leverage the tools or their data. Even the self-service tools, which are to be used by the average business user, require someone to help deploy them. Companies can turn to vendor support teams or third-party consultants to assist if they are unable to bring someone in house.

Data organization: To get the most of analytics solutions, that data needs to be organized. This means that databases should be set up correctly and integrated properly. This may require building a data warehouse, which can store data from a variety of applications and databases in a central location. 

Businesses may need to purchase a dedicated data preparation software as well to ensure that data is joined and is clean for the analytics solution to consume in the right way. In the context of big data, a company might want to specifically consider big data processing and distribution software. This often requires a skilled data analyst, IT employee, or an outside consultant to help ensure data quality is at its finest for easy analysis.

User adoption: It is not always easy to transform a business into a data-driven company. Particularly at more established companies that have done things the same way for years, it is not simple to force analytics tools upon employees, especially if there are ways for them to avoid it. If there are other options, such as spreadsheets or existing tools that employees can use instead of analytics software, they will most likely go that route. However, if managers and leaders ensure that analytics tools are a necessity in an employee’s day to day, then adoption rates will increase.

Which Companies Should Buy Big Data Analytics Software?

As has often been said, data is the fuel that drives modern businesses. Although it is cliche, it no doubt has truth to it. Therefore, businesses across the globe and across industries should consider some sort of analytics solution, such as big data analytics in order to make sense of that data and begin to make data-driven decisions. 

Financial services: Within financial institutions, such as insurance brokerages, banks, and credit unions, it is common for a host of different systems to be used. These companies have data ranging from customer records, to transactions, to market data, and more. With the proliferation of systems comes more data. With a robust analytics solution in place, they can get a better understanding of the data that is being produced from the various systems across the business. As an industry that is heavily regulated, users can benefit from governed access capabilities which can be particularly beneficial, since it can assist in auditing company processes.

Healthcare: Within the space of healthcare, bad data practices might have dire or even deadly consequences. Big data analytics software can help these organizations with having an overarching view of their data, such as patient records, insurance claims, finances, and more. Through the implementation of analytics, healthcare companies can lower risk and costs, and make their billing and collections smarter.

Retail: Retail organizations, whether they be B2C, B2B, D2C, or others, rely on data to make informed decisions. For example, a seller of printers, in order to run a successful business, must keep track of many things such as their inventory, sales, their sales team, and returns. If all of this data is kept siloed within different systems, there is no single source of truth and departments cannot have a conversation around the actual state of the business’ data. With big data analytics software set up and connected to all of the relevant data sources, any retail business can see benefits and make meaningful data-driven decisions.

How to Buy Big Data Analytics Software

Requirements Gathering (RFI/RFP) for Big Data Analytics Software

If a company is just starting out on their analytics journey, g2.com can help in selecting the best software for the particular company and use case. Since the particular solution might vary based on company size and industry, G2 is a great place to sort and filter reviews based on these criteria, along with many more.

As mentioned above, the variety, volume, and velocity of data are vast. Therefore, users should think about how the particular solution fits their particular needs, as well as their future needs as they accumulate more data. 

To find the right solution, buyers should determine pain points and jot them down. These should be used to help create a checklist of criteria. Additionally, the buyer must determine the number of employees who will need to use this software, as this drives the number of licenses they are likely to buy.

Taking a holistic overview of the business and identifying pain points can help the team springboard into creating a checklist of criteria. The checklist serves as a detailed guide that includes both necessary and nice-to-have features including budget, features, number of users, integrations, security requirements, cloud or on-premises solutions, and more.

Depending on the scope of the deployment, it might be helpful to produce a request for information (RFI), a one-page list with a few bullet points describing what is needed from a big data analytics software.

Compare Big Data Analytics Software Products

Create a long list

From meeting the business functionality needs to implementation, vendor evaluations are an essential part of the software buying process. For ease of comparison after all demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor.

Create a short list

From the long list of vendors, it is helpful to narrow down the list of vendors and come up with a shorter list of contenders, preferably no more than three to five. With this list in hand, businesses can produce a matrix to compare the features and pricing of the various solutions.

Conduct demos

To ensure the comparison is thoroughgoing, the user should demo each solution on the shortlist with the same use case and data sets. This will allow the business to evaluate like for like and see how each vendor stacks up against the competition. 

Selection of Big Data Analytics Software

Choose a selection team

As big data analytics software is all about the data, the user must make sure that the selection process is data driven as well. The selection team should compare notes and facts and figures which they noted during the process, such as time to insight, number of visualizations, and availability of advanced analytics capabilities.

Negotiation

Just because something is written on a company’s pricing page, does not mean it is not negotiable (although some companies will not budge). It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to give a discount for multi-year contracts or for recommending the product to others.

Final decision

After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and well received, the buyer can be confident that the selection was correct. If not, it might be time to go back to the drawing board.

What Does Big Data Analytics Software Cost?

Businesses decide to deploy big data analytics software with the goal of deriving some degree of a return on investment (ROI).

Return on Investment (ROI)

As they are looking to recoup their losses that they spent on the software, it is critical to understand the costs associated with it. As mentioned above, this software is typically billed per user, which is sometimes tiered depending on the company size. More users will typically translate into more licenses, which means more money.

Users must consider how much is spent and compare that to what is gained, both in terms of efficiency as well as revenue. Therefore, businesses can compare processes between pre- and post-deployment of the software to better understand how processes have been improved and how much time has been saved. They can even produce a case study (either for internal or external purposes) to demonstrate the gains they have seen from their use of the big data analytics tool.

Implementation of Big Data Analytics Software

How is Big Data Analytics Software Implemented?

Implementation differs drastically depending on the complexity and scale of the data. In organizations with vast amounts of data in disparate sources (e.g., applications, databases, etc.), it is often wise to utilize an external party, whether that be an implementation specialist from the vendor or a third-party consultancy. With vast experience, they can help businesses understand how to connect and consolidate their data sources and how to use the software efficiently and effectively.

Who is Responsible for Big Data Analytics Software Implementation?

It may require a lot of people, or many teams, to properly deploy an analytics platform. This is because data can cut across teams and functions. As a result, it is rare that one person or even one team has a full understanding of all of a company’s data assets. With a cross-functional team in place, a business can piece together their data and begin the journey of analytics, starting with proper data preparation and management.