Spark SQL Reviews & Product Details

querying data from both in RDDs and external sources at ease. And able to query historical data with only Spark engine reducing the dependency of different engine. Review collected by and hosted on G2.com.

limitations in real time processing of the data. Review collected by and hosted on G2.com.

I appreciate Spark SQL's robust query optimization and unified data processing capabilities, providing a streamlined and high-performance solution for complex analytics tasks." Review collected by and hosted on G2.com.

While Spark SQL impresses with robust query optimization and unified data processing, occasional challenges in resource management and the absence of some conventional SQL functions may require careful consideration in specific analytics scenarios. Review collected by and hosted on G2.com.

The ability to manipulate the spark data frame using SQL queries. Review collected by and hosted on G2.com.

The syntax can seem a bit different at the start. Review collected by and hosted on G2.com.

It can run on dataframe as well as act as a distributed query engine.

Unlike the normal SQL, it is a module which is used for structured databases.

It allows Hadoop Hive queries to run like 100 times faster on existing deployment of data and therefore allowing big data to be handled efficiently. Review collected by and hosted on G2.com.

There is no file management system of it's own and so it needs to be connected with one.

There are no automatuc file optimisation techniques and we have to optimise our codes manylually.

There is no support for real time processing and also there are issues with small files while working with hadoop. Review collected by and hosted on G2.com.

It allows all types of SQL commands like mysql,mssql,posgresql and many with faster processing Review collected by and hosted on G2.com.

There isn't anything i disliked about Spark SQL Review collected by and hosted on G2.com.

Partitioning and The way to use Memory as well as Disk.

Cache is one of the best features of Spark SQL.

And use of temporary table. Review collected by and hosted on G2.com.

Limitated support for transaction.

Performance impact with small Dataset.lack of native support of all SQL functions like proprietary extension. Review collected by and hosted on G2.com.

What I love about Spark SQL is its seamless integration with the Spark ecosystem, enabling me to leverage distributed computing capabilities and work with structured data using SQL syntax. The optimiser and query planner, Catalyst, ensures efficient execution. At the same time, its wide range of data source support and integration with other Spark components make it a powerful tool for end-to-end data processing. Review collected by and hosted on G2.com.

What I dislike are :

Debugging complexity: Challenging to debug complex queries and optimize query plans.

Performance tuning: Fine-tuning query performance requires in-depth knowledge and experimentation.

Compatibility limitations: Not fully compatible with all SQL dialects and databases.

Limited support for complex analytics: Some advanced SQL features may be unsupported or require custom implementations.

Despite these challenges, I think Spark SQL is a powerful tool for distributed data processing with efficient and scalable capabilities. Review collected by and hosted on G2.com.

Easy to understand and using spark power via easy sqlskils ,this terminology easy to learn and any one who have the basic sql skills can easily work ,only some of the things from sql is different but most of the things are same Review collected by and hosted on G2.com.

In terms of downside spark sql , the thing which I don't like is that if I want to use some custom function either I need to use udf or udaf which is a little hectic think and it need good coding knowledge Review collected by and hosted on G2.com.

Allowing users to seamlessly switch between different data processing and works with structured and semi structured data efficiently.

Uses catalyst engine to enable spark sql to deliver fast and efficient query processing across large datasets. Review collected by and hosted on G2.com.

Error messages during the query execution provided by Spark sql can be challenging to interpret.

Users who are not familiar with databases and sql concepts require some time and effort to grasp. Review collected by and hosted on G2.com.

I have been using Spark SQL for quite some time now, and I must say it has completely revolutionized the way I analyze and query large-scale datasets. With its impressive capabilities and seamless integration with Apache Spark, Spark SQL has become an essential tool in my data processing toolkit. Review collected by and hosted on G2.com.

One area where Spark SQL can be challenging is its learning curve. Review collected by and hosted on G2.com.