All these are the top big data tools and technologies to know about in 2023. Let’s take a look at each of them. Check out the online Big Data course to learn more.
1.Hive
For reading, writing, and managing huge data sets in distributed storage systems, Hive is a data warehouse infrastructure program based on SQL. It was developed by Facebook but was subsequently open-sourced to Apache, which keeps the technology updated.
Hive processes structured data and works on top of Hadoop. It is primarily designed for data summarization, analysis, and querying massive amounts of data. Hive is defined by its creators as scalable, quick, and versatile even though it cannot be used for online transaction processing, real-time updates, queries, or processes that call for low-latency data retrieval.
The following are additional crucial characteristics:
- standard SQL feature for data analytics and querying; an integrated system to assist users in applying structure to various types of data formats; and
- access to files in HDFS as well as those kept in other platforms, including the Apache HBase database.
2.Hadoop
Hadoop was created as a ground-breaking big data technology to assist in handling the expanding volumes of structured, unstructured, and semi-structured data. It is a distributed framework for storing data and running applications on clusters of commodity hardware. It was initially introduced in 2006 and was almost immediately associated with big data; it has since been somewhat supplanted by newer technologies but is still in widespread use.
Hadoop consists of four basic components:
- the Hadoop Distributed File System (HDFS), which organises data into blocks for storage on cluster nodes, controls access to the data, and uses replication to stop data loss;
- Hadoop MapReduce, a built-in batch processing engine that divides up large computations and runs them on different nodes for speed and load balancing;
- Hadoop Common, a shared set of utilities and libraries;
- and YARN, short for Yet Another Resource Negotiator, are the three other main components of the Hadoop ecosystem.
3.Hudi
Hudi, which stands for Hadoop Upserts, Deletes, and Incrementals, is pronounced “hoodie.” Large analytics data sets are ingested and stored using HDFS and other Hadoop-compatible file systems using this open-source technology, which is also managed by Apache.
Hudi was initially created by Uber and is intended to offer quick and low-latency data intake and preparation capabilities. Additionally, it contains a data management architecture that businesses can employ to:
- streamline the construction of data pipelines and incremental processing of data;
- enhancing big data systems’ data quality; and
control the progression of data sets.
4.Iceberg
An open table format called Iceberg is used to manage data in data lakes, and it does this in part by tracking individual data files in tables as opposed to directories. Iceberg, which was developed by Netflix for use with their petabyte-sized tables, is now an Apache project. On the website for the project, it is said that Iceberg “is used in production where a single table can contain tens of petabytes of data.”
The Iceberg table format was created to be an improvement over the default layouts seen in tools like Hive, Presto, Spark, and Trino. It behaves similarly to SQL tables found in relational databases. It does, however, allow for the use of numerous engines that share the same set of data. Other noteworthy features include:
- The ability to “time travel” allows for reproducible queries using the same table snapshot;
- hidden data partitioning eliminates the need for users to maintain partitions;
- and schema evolution for changing tables without having to rewrite or migrate data.
5.Kafka
According to Apache, more than 80% of Fortune 100 firms and thousands of other organisations use the distributed event streaming platform Kafka for mission-critical applications, streaming analytics, high-performance data pipelines, and data integration. Kafka is a framework for storing, reading, and analysing streaming data, to put it simply.
The technology holds the data streams so they can be used elsewhere and decouples the data streams from the systems. It operates in a distributed environment and communicates with other computers and applications using the fast TCP network protocol. LinkedIn developed Kafka before handing it off to Apache in 2011.
The following are some of Kafka’s most important elements:
- a collection of five fundamental programming interfaces (APIs) for Java and Scala;
- as well as fault tolerance for both servers and clients in Kafka clusters and elastic scaling to 1,000 brokers (or storage servers) per cluster.
6.Kylin
Kylin is a large data analytics and distributed data warehouse platform. It offers an OLAP engine for online analytical processing that can handle very big data sets. Kylin can grow easily to accommodate those enormous data loads because it is built on top of other Apache technologies like Hadoop, Hive, Parquet, and Spark, according to its supporters.
Additionally, it responds to queries quickly, in milliseconds. In addition, Kylin interacts with Tableau, Microsoft Power BI, and other BI applications and offers an ANSI SQL interface for multidimensional large data analysis. eBay created Kylin at first, and in 2014 it was made available as an open-source technology. The following year, it was made a top-level project under Apache. The following are other features it offers:
- the ability to construct custom UIs on top of the Kylin core;
- task management and monitoring features;
- and the precalculation of multidimensional OLAP cubes to speed analytics.
Conclusion
You can learn more about other tools by checking out the Big Data online training.
