DataQ utilizes world-class testing tools to analyze big data applications efficiently. Let’s take a look at them.
Hadoop Distribution File System (HDFS)
Hadoop is an open-source processing framework that handles pools of big data. Hadoop Distribution File System (HDFS) is the primary data storage system used by the Hadoop applications. It can accommodate apps that have gigabytes or terabytes of datasets. It has been designed to detect the fault and make an auto-recovery quickly.
Hortonworks is an open-source framework for distributed storage. Cloudera developed this technology, and it can efficiently process large and multi-source datasets. It enables you to conveniently gain insights from structured and unstructured data of the big data system.
Cassandra is an open-source distributed database management system. It can efficiently handle large amounts of data across many commodity servers. It offers high availability with no single point of failure. As a result, Cassandra has become one of the most reliable platforms for handling critical data.
Google BigQuery is a server-less data warehouse. It allows you to make rapid SQL queries. Also, it enables you to perform scalable analysis of massive datasets. Google BigQuery offers a flexible, scalable, and multi-cloud analytics solution. It allows you to derive valuable insights from large datasets easily. As a result, you can quickly make effective business decisions.
Snowflake is a cloud-based data warehouse. It is used for efficiently processing large-scale data. It enables you to easily discover and securely share live governed data across your business. As a result, users at all levels can make data-driven decisions.