What is Apache Tajo?

Apache Tajo is a warehouse system which is an open-source and  distributed  for large-scale data processing and analysis. It is designed to enable fast and efficient querying and analysis of large volumes of data and is particularly well-suited for running complex, large-scale SQL queries.

Tajo is built on top of a distributed, in-memory data processing engine that allows it to process data extremely quickly. It can be used with a variety of data sources and formats, including structured and unstructured data, and can be integrated with other tools and systems in the Apache Hadoop ecosystem.

Tajo is highly scalable and can handle very large volumes of data, making it a popular choice for organizations that need to process and analyze large amounts of data quickly. It is also highly flexible, allowing users to define and execute custom functions and queries.

Apache Tajo is an Apache project and is a part of the Apache Hadoop ecosystem. It is widely used for data processing and analysis in a variety of industries, including finance, healthcare, and e-commerce.

A relational database called a “data warehouse” is made for query and analysis rather than transaction processing. It is a subject-focused, cohesive, time-varying, and non-volatile data set. Although relational data quantities are growing daily, this information aids analysts in making informed decisions for the firm.

To get around these problems, distributed data warehouse systems pool data from several data sources for online analytical processing (OLAP). There may be one or more organizations represented by each data warehouse. It carries out scalability and load balancing. Replicated and disseminated centrally is metadata.

A distributed data warehousing system called Apache Tajo has its own query execution engine in place of the MapReduce framework and uses Hadoop Distributed File System (HDFS) as the storage layer.