In the spotlight

Improve security for Hive Data using IBM® Db2 Big SQL

Are you needing to improve security for Hive Data? Or looking for role-based access control and row and column level security for Hive? Learn how Hive can be used with IBM Db2 Big SQL on Hadoop providing a secure environment.  

Access Apache Hive Data Faster and More Securely with Db2 Big SQL

For security, Db2 Big SQL provides unique capabilities like row-level access control, column level security, and role-based access control. Db2 Big SQL makes access to Hive data faster, and more secure.

What is Apache Hive?

Although Apache™  Pig™  can be quite a powerful and simple language to use, the downside is that it’s something new to learn and master. A runtime Apache Hadoop support structure was developed allowing fluent SQL users (which is commonplace for relational data-base developers) to leverage the Hadoop platform. Apache Hive, allows SQL developers to write Hive Query Language (HQL) statements that are similar to standard SQL ones. HQL is limited in commands it understands, but still useful.

As with any database management system (DBMS), you can run your Hive queries from a command line interface (known as the Hive shell), from a Java Database Connectivity (JDBC) or Open Database Connectivity (ODBC) application leveraging the Hive JDBC/ODBC drivers. You can run a Hive Thrift Client, within applications written in C++, Java, PHP, Python, or Ruby (much like you can use these client-side languages with embedded SQL to access a database such as Db2 or Informix).

Hive looks very much like traditional database code with SQL access. However, because Hive is based on Apache™ Hadoop™ and hive operations, there are several key differences.

The first is that Hadoop is intended for long sequential scans, and because Hive is based on Hadoop, you can expect queries to have a very high latency (many minutes). This means that Hive would not be appropriate for applications that need very fast response times, as you would expect with a database such as Db2. Finally, Hive is read-based and therefore not appropriate for transaction processing that typically involves a high percentage of write operations.

If you're interested in SQL on Hadoop, in addition to Hive, IBM offers Db2 Big SQL which makes accessing Hive datasets faster and more secure. Checkout our videos, below, for a quick overview of Hive and Db2 Big SQL.



A hybrid SQL engine for Apache Hadoop that concurrently exploits Hive, HBase and Spark using a single database connection or query.

IBM Analytics for Apache Spark

IBM Analytics for Apache® Spark™ gives you the power of Apache Spark with integrated Jupyter Notebooks for faster iteration and answers. The service is fully-managed, which gives you immediate access to hassle-free Apache Spark.

IBM InfoSphere Federation Server

Access and integrate diverse data and content sources as if they were a single resource - regardless of where the information resides.


The data warehouse evolved: A foundation for analytical excellence

ReExplore a Best-in-Class approach to data management and how companies are prioritizing data technologies to drive growth and efficiency.

Understanding big data beyond the hype

Read this practical introduction to the next generation of data architectures that introduces the role of the cloud and NoSQL technologies and discusses the practicalities of security, privacy and governance.

IBM Db2 Big SQL data sheet

With Spark SQL, the fastest open source SQL engine available, amplify the power of Apache Hadoop on IBM BigInsights to create insight. Spark SQL is helping make big data environments faster than ever.

Accessing tables created in Hive and files added to HDFS from Db2 Big SQL

This blog will give an overview of procedures that can be taken if immediate access to these tables are needed, offer an explanation of why those procedures are required and also give an introduction to some of the features in Db2 Big SQL in this area.

Engage with an expert

Schedule a one-on-one call with an expert to learn about the IBM Hortonworks relationship and how we can help you extend data science and machine learning across the Apache Hadoop ecosystem.