Apache Parquet is designed for efficient as well as performant flat columnar storage format of data compared to row based files like CSV or TSV files. Parquet uses the record shredding and assembly algorithm which is superior to simple flattening of nested namespaces. Parquet is optimized to work with complex data in bulk and features different ways for efficient data compression and encoding types. This approach is best especially for those queries that need to read certain columns from a large table. Parquet can only read the needed columns therefore greatly minimizing the IO.

A high performance columnar OLAP database management system for real-time analytics using SQL. ClickHouse can be customized with a new set of efficient columnar storage engines, and has realized rich functions such as data ordered storage, primary key indexing, sparse indexing, data sharding, data partitioning, TTL, and primary and backup replication.

HBase is a high reliability, high performance, column-oriented, and scalable distributed database. However, the READ/write performance deteriorates when a large amount of concurrent data or existing data is generated. You can use the following methods to improve the HBase search speed.

Rows in HBase are sorted lexicographically by row key. This design optimizes for scans, allowing you to store related rows, or rows that will be read together, near each other. However, poorly designed row keys are a common source of hotspotting. Hotspotting occurs when a large amount of client traffic is directed at one node, or only a few nodes, of a cluster. This traffic may represent reads, writes, or other operations. The traffic overwhelms the single machine responsible for hosting that region, causing performance degradation and potentially leading to region unavailability. This can also have adverse effects on other regions hosted by the same region server as that host is unable to service the requested load. It is important to design data access patterns such that the cluster is fully and evenly utilized.

YARN is a generic resource-management framework for distributed workloads; in other words, a cluster-level operating system. Although part of the Hadoop ecosystem, YARN can support a lot of varied compute-frameworks (such as Tez, and Spark) in addition to MapReduce.

Apache Spark has its architectural foundation in the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. The Dataframe API was released as an abstraction on top of the RDD, followed by the Dataset API.

Apache Spark has its architectural foundation in the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. The Dataframe API was released as an abstraction on top of the RDD, followed by the Dataset API.

Hive is a Hadoop-based data warehouse tool that maps structured data files into a database table and provides complete SQL query functionality that converts SQL statements into MapReduce tasks for execution. It is very suitable for statistical analysis of data warehouse.