zlib is a software library used for data compression as well as a data format. zlib was written by Jean-loup Gailly and Mark Adler and is an abstraction of the DEFLATE compression algorithm used in their gzip file compression program. zlib is also a crucial component of many software platforms, including Linux, macOS, and iOS. It has also been used in gaming consoles such as the PlayStation 4, PlayStation 3, Wii U, Wii, Xbox One and Xbox 360.
bzip2 is a free and open-source file compression program that uses the Burrows–Wheeler algorithm. It only compresses single files and is not a file archiver. It relies on separate external utilities for tasks such as handling multiple files, encryption, and archive-splitting.
A bitmap index is a special kind of database index that uses bitmaps.
Fast Infoset is an international standard that specifies a binary encoding format for the XML Information Set as an alternative to the XML document format. It aims to provide more efficient serialization than the text-based XML format.
Apache Hadoop is a collection of open-source software utilities that facilitates using a network of many computers to solve problems involving massive amounts of data and computation. It provides a software framework for distributed storage and processing of big data using the MapReduce programming model. Hadoop was originally designed for computer clusters built from commodity hardware, which is still the common use. It has since also found use on clusters of higher-end hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common occurrences and should be automatically handled by the framework.
A column-oriented DBMS or columnar DBMS is a database management system (DBMS) that stores data tables by column rather than by row. Benefits include more efficient access to data when only querying a subset of columns, and more options for data compression. However, they are typically less efficient for inserting new data.
HBase is an open-source non-relational distributed database modeled after Google's Bigtable and written in Java. It is developed as part of Apache Software Foundation's Apache Hadoop project and runs on top of HDFS or Alluxio, providing Bigtable-like capabilities for Hadoop. That is, it provides a fault-tolerant way of storing large quantities of sparse data.
Cassandra is a free and open-source, distributed, wide-column store, NoSQL database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. Cassandra offers support for clusters spanning multiple datacenters, with asynchronous masterless replication allowing low latency operations for all clients. Cassandra was designed to implement a combination of Amazon's Dynamo distributed storage and replication techniques combined with Google's Bigtable data and storage engine model.
Avro is a row-oriented remote procedure call and data serialization framework developed within Apache's Hadoop project. It uses JSON for defining data types and protocols, and serializes data in a compact binary format. Its primary use is in Apache Hadoop, where it can provide both a serialization format for persistent data, and a wire format for communication between Hadoop nodes, and from client programs to the Hadoop services. Avro uses a schema to structure the data that is being encoded. It has two different types of schema languages; one for human editing and another which is more machine-readable based on JSON.
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data query and analysis. Hive gives an SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop. Traditional SQL queries must be implemented in the MapReduce Java API to execute SQL applications and queries over distributed data. Hive provides the necessary SQL abstraction to integrate SQL-like queries (HiveQL) into the underlying Java without the need to implement queries in the low-level Java API. Since most data warehousing applications work with SQL-based querying languages, Hive aids the portability of SQL-based applications to Hadoop. While initially developed by Facebook, Apache Hive is used and developed by other companies such as Netflix and the Financial Industry Regulatory Authority (FINRA). Amazon maintains a software fork of Apache Hive included in Amazon Elastic MapReduce on Amazon Web Services.
Within database management systems, the RCFile is a data placement structure that determines how to store relational tables on computer clusters. It is designed for systems using the MapReduce framework. The RCFile structure includes a data storage format, data compression approach, and optimization techniques for data reading. It is able to meet all the four requirements of data placement: (1) fast data loading, (2) fast query processing, (3) highly efficient storage space utilization, and (4) a strong adaptivity to dynamic data access patterns.
Apache Drill is an open-source software framework that supports data-intensive distributed applications for interactive analysis of large-scale datasets. Built chiefly by contributions from developers from MapR, Drill is inspired by Google's Dremel system. Drill is an Apache top-level project. Tom Shiran is the founder of the Apache Drill Project. It was designated an Apache Software Foundation top-level project in December 2016.
Apache Impala is an open source massively parallel processing (MPP) SQL query engine for data stored in a computer cluster running Apache Hadoop. Impala has been described as the open-source equivalent of Google F1, which inspired its development in 2012.
Apache Kudu is a free and open source column-oriented data store of the Apache Hadoop ecosystem. It is compatible with most of the data processing frameworks in the Hadoop environment. It provides completeness to Hadoop's storage layer to enable fast analytics on fast data.
FlatBuffers is a free software library implementing a serialization format similar to Protocol Buffers, Thrift, Apache Avro, SBE, and Cap'n Proto, primarily written by Wouter van Oortmerssen and open-sourced by Google. It supports “zero-copy” deserialization, so that accessing the serialized data does not require first copying it into a separate part of memory. This makes accessing data in these formats much faster than data in formats requiring more extensive processing, such as JSON, CSV, and in many cases Protocol Buffers. Compared to other serialization formats however, the handling of FlatBuffers requires usually more code, and some operations are not possible.
Apache Arrow is a language-agnostic software framework for developing data analytics applications that process columnar data. It contains a standardized column-oriented memory format that is able to represent flat and hierarchical data for efficient analytic operations on modern CPU and GPU hardware. This reduces or eliminates factors that limit the feasibility of working with large sets of data, such as the cost, volatility, or physical constraints of dynamic random-access memory.
Apache ORC is a free and open-source column-oriented data storage format. It is similar to the other columnar-storage file formats available in the Hadoop ecosystem such as RCFile and Parquet. It is used by most of the data processing frameworks Apache Spark, Apache Hive, Apache Flink and Apache Hadoop.
Apache CarbonData is a free and open-source column-oriented data storage format of the Apache Hadoop ecosystem. It is similar to the other columnar-storage file formats available in Hadoop namely RCFile and ORC. It is compatible with most of the data processing frameworks in the Hadoop environment. It provides efficient data compression and encoding schemes with enhanced performance to handle complex data in bulk.
Apache IoTDB is a column-oriented open-source, time-series database (TSDB) management system written in Java. It has both edge and cloud versions, provides an optimized columnar file format for efficient time-series data storage, and TSDB with high ingestion rate, low latency queries and data analysis support. It is specially optimized for time-series oriented operations like aggregations query, downsampling and sub-sequence similarity search. The name IoTDB comes from Internet of Things (IoT) Database, which means it was designed as an IoT-native TSDB that resolves the pain points of the typical IoT scenarios, including massive data generation, high frequency sampling, out-of-order data, specific analytics requirements, high costs of storage and operation & maintenance, low computational power of IoT devices.
