Can Big Data be Stored on an SQL Server?

Yes, big data can indeed be stored on an SQL server. Since 2016, SQL Server has introduced the PolyBase feature, which allows it to load or access data directly from existing large Hadoop NOSQL clusters. Large data tables can be partitioned and the partitions can be spread across large SANs or a cloud. The default setting for max memory, if available, is 2,147,483,647 megabytes (MB), and the maximum database size is 524,272 terabytes (TB). By default, SQL Server can change its memory requirements dynamically based on available system resources.

What is Big Data?

Big Data refers to large, complex, and diverse sets of data that can be both structured and unstructured. The term 'big data' implies not only vast amounts of data but also unstructured semantics, meaning that the data often comes from disparate sources and in various formats. This makes fitting it into a strictly structured relational database management system (RDBMS) inconvenient. However, as mentioned earlier, databases in multi-terabyte sizes are far from uncommon.

Can Big Data be Stored on an SQL Server?

Yes, big data can be stored on an SQL server. Mainframe systems have been handling such scenarios for around 40 years now. SQL Server, being a robust and scalable database system, can accommodate large volumes of data and support complex data retrieval operations.

Challenges and Alternatives

Although SQL Server can store big data, there are inherent challenges when dealing with very large datasets. For instance, in relational databases, operations like joins and aggregations become significantly slower with large volumes of data. Secondary indexes, which are typically beneficial, become too expensive to maintain. Consequently, a different approach may be more suitable for handling big data.

Relational Database Management Systems vs. Big Data Technologies

While RDBMSs excel in transactional workloads, they struggle with the high volume and variety of big data. In particular, operations that require complex analytics, such as machine learning and deep data analysis, may become slow and resource-intensive.

Alternatives for Handling Big Data

Apache Cassandra: This is a distributed NoSQL database system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. It is known for its scalability and support for real-time read/write operations.

Apache Spark SQL: This integrates Spark's processing engine with SQL, making it easier to work with large datasets. When paired with Cassandra, Spark SQL can provide a high-performance environment for complex data processing and analytics.

HBase: This is an open-source, distributed, column-oriented database that backs up many of the features of Apache Hadoop. HBase is designed to provide random access to big data and supports rapid scans for deep analysis.

Conclusion

Big data storage on an SQL server is possible, especially with features like PolyBase. However, for optimal performance and handling of large volumes of data, it may be more appropriate to leverage specialized big data technologies such as Apache Cassandra, Apache Spark SQL, or HBase. Each of these solutions has its own strengths and is better suited to specific types of big data workloads.