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. Such large acceptance and acceptability of SQL can be utilized for querying Big data through SQL 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.
Before we delve into the details, let’s understand what SQL, Hadoop and SQL on Hadoop are.
SQL stands for Structured Query Language. It is used for managing data mainly in relational databases (RDBMS). In simple terms, it is a database language to manipulate data like create databases, add or delete rows etc.
Hadoop is a framework with which large volumes of data (mostly Big Data) is stored and processed. Hadoop mainly uses HDFS- Hadoop Distributed file system to store the data and process the data using Map-Reduce, Hive, Pig and a few other facilities.
SQL-on-Hadoop is a powerful technology that brings together the traditional SQL querying approach with the newer Hadoop data framework elements. By leveraging the familiarity and versatility of SQL, SQL-on-Hadoop makes it easier for a wider range of enterprise developers and business analysts to work with Hadoop on commodity computing clusters. With SQL-on-Hadoop, businesses can process large amounts of data quickly and efficiently, gaining insights and making informed decisions based on data-driven analysis
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.
SUGGESTED READ: BIG DATA MANAGEMENT: HADOOP OR SNOWFLAKE
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)
Confused about Data Lake or Data Warehouse or what type of Big Data management tools would be the perfect fit for your organization?
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.
For SQL on Hadoop implementations, Contact us and we will find the best optimum and personalized solution fit for your organization.
