Apache Hadoop is scalable and cost-effective when processing large data volumes, so it's a popular open-source framework for many organizations.
Unfortunately, enterprises with traditional data warehouses are usually based on Structured Query Language (SQL), making the Apache Hadoop ecosystem difficult for users and developers who have relied on SQL queries to extract data.
With Apache Hive's SQL intellect, you can write SQL-like queries called hive query language (HQL) to extract data from Hadoop without learning another language, taking advantage of Hadoop's power to save you time and money.
Because Hive is built on top of Hadoop, it offers the same speed, flexibility and scalability that Hadoop is known for.
Using an SQL-like interface, developers can write HQL statements similar to standard SQL statements for data query and analysis, saving them the need to learn a new programming language.
Get to the bottom of data by extracting and viewing it in various dimensions-consolidated, drilled-down or sliced and diced-with online analytical processing (OLAP) capabilities.
IBM Db2 Big SQL is a hybrid SQL engine for Apache Hadoop. It can concurrently exploit Hive, HBase and Spark using a single database connection or query.
Optimize open-source technologies runtime-including Apache Spark, Anaconda and Python-and gain insights from data at its source.
Access and integrate diverse data and content sources as a single resource regardless of where the information resides.
IBM and Cloudera have partnered to offer an industry-leading, enterprise-grade Hadoop distribution, including an integrated ecosystem of products and services to support faster analytics at scale.
Apache Spark is a lightning-fast, open-source data-processing engine for machine learning and AI applications, backed by the largest open source community in big data. IBM analytics services for Apache Spark give you the power of a hassle-free Spark experience with integrated Jupyter Notebooks for faster integration and answers.
Explore a best-in-class approach to data management and how companies are prioritizing data technologies to drive growth and efficiency.
Read this practical introduction to the next generation of data architectures. It introduces the role of the cloud and NoSQL technologies and discusses the practicalities of security, privacy and governance. (PDF 6.5 MB)