Data Lake

What is a data lake?

A data lake is a centralized location where an organization can store structured and unstructured data. This system allows data to be stored as-is and can run analytics that help with decision making. Data lakes help companies derive more value from their data.

Companies often use relational databases to store and manage data so it can be easily accessed and the information they need can be found.

Logiciel Data Lake

Logiciel mentionnant data lake comme fonctionnalité ou terme.

Azure Data Lake Store

AWS Lake Formation

Amazon Simple Storage Service (S3)

Azure Data Lake Analytics

Dremio

Snowflake

Data lake use cases

Data lakes' low cost and open format make them essential for modern data architecture. Potential use cases for this data storage solution include:

• Media and entertainment: Digital streaming services can boost revenue by improving their recommendation system, influencing users to consume more services. 
• Telecommunications: Multinational telecommunications companies can use a data lake to save money by building churn-propensity models that lessen customer churn.
• Financial services: Investment firms can use data lakes to power machine learning, enabling the management of portfolio risks as real-time market data becomes available. 

Data lake benefits

When organizations can harness more data from various sources within a reasonable time frame, they can collaborate better, analyze information, and make informed decisions. Key benefits are explained below:

• Improve customer interactions. Data lakes can combine customer data from multiple locations, such as customer relationship management, social media analytics, purchase history, and customer service tickets. This informs the organization about potential customer churn and ways to increase loyalty.

• Innovate R&D. Research and development (R&D) teams use data lakes to better test hypotheses, refine assumptions, and analyze results.

• Increase operational efficiency. Companies can easily run analytics on machine-generated internet of things (IoT) data to identify potential ways to improve processes, quality, and ROI for business operations.

• Power data science and machine learning. Raw data is transformed into structured data used for SQL analytics, data science, and machine learning. As costs are low, raw data can be kept indefinitely. 
• Centralize data sources. Data lakes eliminate issues with data silos, enabling easy collaboration and offering downstream users a single data source.
• Integrate diverse data sources and formats. Any data can be stored indefinitely in a data lake, creating a centralized repository for up-to-date information.
• Democratize data through self-service tools. This flexible storage solution enables collaboration between users with varying skills, tools, and languages. 

Data lake challenges

While data lakes have their benefits, they do not come without challenges. Organizations implementing data lakes should remain aware of the following potential difficulties:

• Reliability issues: These problems arise due to difficulty combining batch and streaming data and data corruption, among other factors.
• Slow performance: The larger the data lake, the slower the performance of traditional query engines. Metadata management and improper data partitioning can result in bottlenecks.
• Security: Because visibility is limited and the ability to delete or update data is lacking, data lakes are difficult to secure without additional measures.

Data lake basic elements

Data lakes act as a single source of truth for data within an organization. The basic elements of a data lake involve the data itself and how it is used and stored. 

• Data movement: Data can be imported in its original form in real-time, no matter the size. 
• Analytics: Information accessible to analysts, data scientists, and other relevant stakeholders within the organization. The data can be accessed with the employee’s analytics tool or framework of choice.
• Machine learning: Organizations can generate valuable insights in a variety of types. Machine learning software is used to forecast potential outcomes that inform action plans within the organization.

Data lake best practices

Data lakes are most effective when they are well organized. The following best practices are useful for this purpose:

• Store raw data. Data lakes should be configured to collect and store data in its source format. This gives scientists and analysts the ability to query data in unique ways. 
• Implement data lifecycle policies. These policies dictate what happens to data when it enters the data lake and where and when that data is stored, moved, and/or deleted.
• Use object tagging: This allows data to be replicated across regions, simplifies security permissions by providing access to objects with a specific tag, and enables filtering for easy analysis.

Data lake vs. data warehouse

Data warehouses are optimized to analyze relational data coming from transactional systems and line of business applications. This data has a predefined structure and schema, allowing faster SQL queries. This data is cleaned, enriched, and transformed into a single source of truth for users.

Data lakes store relational data from line of business applications and non-relational data from apps, social media, and IoT devices. Unlike a data warehouse, there is no defined schema. A data lake is a place where all data can be stored, in case questions arise in the future.