Benefits Of SQL on Hadoop
The standard for working and managing data for the past few decades has been the SQL language. The enterprise has been largely dominated by SQL. From operational workloads to reporting to analytics, SQL can be found everywhere. And, now this standard will continue on Hadoop.
With the help of major initiatives, Hadoop has been brought from its batch-oriented roots to the interactive capabilities that allow it to deliver enhanced performance in SQL engines and with distributed in-memory engines.
SQL on Hadoop- Why Is It Necessary?
For many organizations, the querying support makes for a crucial factor that allows the deployment of Hadoop cluster a feasible option for them. Without an SQL level on top of Hadoop, many organizations wouldn’t go for Hadoop implementations.
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Features and Benefits provided by SQL on Hadoop implementation
Rich and compliant SQL dialect: This allows the SQL application to be powerful as well as portable. This, in turn, enables it to successfully leverage a massive ecosystem of SQL-based data analysis and data visualization tools.
TPC-DS specification compliance: Compliance of TPC-DS will ensure that the different classes of SQL queries will be handled. This will enable a wide range of use cases and will help the business implement the enterprise-class in a smooth and elegant fashion.
Flexible and efficient joins: The workloads for Off-load enterprise data warehouse has a significantly low cost of ownership.
Deep learning and machine learning capabilities are integrated: This will enable use cases in SQL where statistical, mathematical and machine learning algorithms will be required.
Data federation capabilities: The data federation capabilities will benefit businesses by reducing the data refactoring costs when implementing end-to-end analytics use case by leveraging assets of the diverse enterprise and external data.
Fault tolerance and high availability: It ensures business continuity along with off-loading of more business-critical analytics from the enterprise data warehouse (EDW)
Native Hadoop file format support: This feature will allow organizations to reduce the effort taken for ETL and data movement. This will have a direct correlation to the lower cost of ownership of the analytics solution.
Native Hadoop management with Apache Ambari: This will enable organizations to reduce the total cost associated with the management of the complete Hadoop stack. This will also eliminate vendor lock-in issues with proprietary management interfaces.
Hadoop was initially tied to MapReduce programming. But that’s now a thing of the past. According to the Gartner analyst Merv Adrian, SQL the programming language used with the mainstream database will become the primary analytics agent in Hadoop data stores.
The pairing will go on to enable hordes of SQL developers, as well as other users who are apt with SQL to write queries for Hadoop in a way that’s familiar to them.