Microsoft recently announced the preview release of SQL Server 2019, which includes a new deployment option called Big Data Cluster. This version of SQL Server is designed to handle big data workloads and offers elastic scale and extended artificial intelligence capabilities. One of the key features of SQL Server Big Data Cluster is its integration with Hadoop and Spark, two popular technologies in the big data space.

But why is it important for SQL Server to be tightly integrated with Hadoop and Spark? The answer lies in the value that structured query language (SQL) brings to big data processing, analytics, and application workflows. SQL has proven to be a dominant force in big data processing, with companies like Facebook and Uber relying on SQL frameworks to analyze massive datasets.

In the past, relational databases faced challenges in handling large datasets, leading to the emergence of distributed systems like Hadoop. Hadoop’s distributed file system (HDFS) revolutionized big data storage, while its MapReduce framework enabled computations on HDFS. However, analyzing structured data with MapReduce was tedious, which led to the development of SQL-on-Hadoop options like Hive.

While SQL-on-Hadoop systems eliminated the need for writing complex MapReduce code, they were primarily used for batch processing and lacked the speed required for interactive queries. This is where Spark comes in. Spark offers in-memory capabilities and an advanced execution engine that can deliver performance up to 10 times faster than Hadoop MapReduce. It also allows for complex SQL logic integration into various big data processes.

Recognizing the importance of Hadoop and Spark in the big data landscape, SQL Server 2019 Big Data Cluster has been designed to integrate these technologies. By doing so, SQL Server becomes a one-stop-shop tool for handling big and unstructured data. It can now handle petabytes or exabytes of data, scale-out compute for data processing and machine learning, and analyze data in unstructured formats.

With the integration of Hadoop and Spark, SQL Server Big Data Cluster offers a unified platform for various big data use cases. It can handle structured data from big data streams and effectively manage and query high-volume data entities like customers, accounts, products, and marketing campaigns. This integration allows SQL Server to deliver end-to-end solutions for a wide range of big data scenarios, from reporting to AI at scale.

In conclusion, the integration of Hadoop and Spark into SQL Server Big Data Cluster brings significant benefits to organizations dealing with big data. It allows for seamless data integration across multiple sources, enables modern and logical data warehouses, and provides powerful analytics capabilities. SQL Server’s integration with Hadoop and Spark positions it as a comprehensive tool for handling big data workloads and delivering valuable insights.