Big data is data so large, that the traditional data processing software can not handle. With the increase in data, generated from social media, business activity, and the internet, new tools that can handle big data query processing, and analysis are developed. Here are the top tools or technologies every big data scientist should know.
Apache Hoodoop is an open-source solution, software for reliable and scalable computing. It allows the processing of big datasets across clusters of high computing computers. And is utilities simple models to achieve that processing fit. You can scale up from a single server to theoretically thousands of computers communicating within a network.
As the name suggests, Microsoft HDInsight is a big data technology build by Microsoft. It relies on the open-source Apache Hadoop. HDInsight has the capability, through Azure, to process very large datasets and give you the data insights that would normally be impossible with traditional data processing software and systems.
- Integrates with Azure services
- Chooses your won development environment
- Spin up big data clusters on demand
Presto is a distributed query engine for big datasets. With your big data, you need a high-performance query engine to effectively query your data for insights.
- High performance – Preto is a parallel and distributed query engine. It is built for efficient, low latency analytics.
- Versatile – There are various use cases supported by Presto.
- Open source – It is driven by a community of developers.
- Works with other existing business intelligent systems
- Query federation – The ability to query data from multiple data points
NoSQL is a term used to refer to non-relational databases. These kinds of databases store information in non-table format. Related data is nested within a single data structure. NoSQL databases have better performance in storing large data (big data). There are many open-source NoSQL databases available to analyze big data, such as MongoDB.
Types of NoSQL databases
- Document databases,
- Key-value databases
- Wide-column stores
- Graph databases
5. Apache Sqoop
Apache Sqoop is a data transferring tool. As the name suggests, Sqoop was developed by Apache and is used to transfer big data between structured datastores and Apache Hoodoop.
You can download Apache Sqoop here.
Hive is a data ware housing technology developed by Apache. It’s main purpose is to facilitate the reading, writing, and managing of large datasets residing in distributed storage using SQL. A command line tool and JDBC driver are provided to connect users to Hive.
PolyBase is a tool that gives your SQL Server instance the capability to process Transact-SQL queries that deals with data from external data sources
Why use PolyBase
Joining a SQL Server data with external data sources was technically very hard. PolyBase makes joining easy by using T-SQL to join the data.
What polybase does
- Query data stored in Hadoop from SQL Server
- Query data stored in Azure Blob Storage
- Import data from Hadoop, Azure (Microsoft)
- Export data to Hadoop, Azure
- Integrate with Business intelligence tools.
It is a unified analytics engine for large-scale data processing. Spark provides in-memory computing with speed. It also has python, java, and scala API for easy development. Spark combines SQL, streaming, and complex analytics into one tool. The other power of spark is that it runs everywhere, Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources.
Hunks makes large datasets accessible, usable, and valuable to the people with speed. Use Hunk 6.2 to turn underutilized data into valuable insights in no time.
10. Rapid Miner
RapidMiner is a big data tool that provides an environment for data preparation, machine learning, deep learning, text mining, and predictive analytics.