The Enterprise data management market is now flooded with a range of Big Data technologies and tools. This helps bring in more cost efficiency, time management, and better results in terms of analytical tasks. This article will discuss some of the best available big data tools and their real-time applications in enterprise data analytics.
Table of Contents
Top 8 Big Data tools
As all of us into big data know, Apache Hadoop is library software of the software in the big data ecosystem. It enables distributed processing of really huge data sets across various clusters of machines. This is one of the top-notch big data tools designed to easily scale up the database from one server to thousands of servers in the cluster. Let us see the key features of Hadoop as below.
- Authentication of the improvements alongside using proxy servers.
- Supporting POSIX-model filesystem with extended attributes
- Specificationsfor Hadoop Compatible file systems
- Technologies and tools for big data management, which offers a solid ecosystem well suited for the analytical needs of enterprise developers
- More flexibility in terms of data processing
- Faster and easier data processing with many add-on tools.
Here are a few real-time use cases of Hadoop:
Financial companies tend to use analytics for their risk assessment, investment modeling, and to create trading algorithms. Hadoop can help build such applications accurately. The retailers also use Hadoop in real-time for analyzing structured or unstructured for better understanding their customers and serve them personally. Also, in areas like the energy industry, which are asset-intensive, Hadoopanalytics can be effectively used for predictive maintenance.
Storm is big open-source data computational software, which is also known as one of the best available big data tools offering a fault-tolerant processing approach. It is also a system with real-time data streaming computation capabilities.
Storm features are:
- One among the best available tools for big data management.
- It is benchmarked as one of the best tools to process about 1 million 100-byte messages in each second per node.
- Capable of handling various big data technologies and tools with parallel calculations which run across a large set of machines.
- It automatically restarts in case of the death of a given node.Likewise, the order will automatically be restarted on another node.
- Storm also ensures that each data unit is processed at least once or exactly once.
- Once after deployment, Strom will remain the easiest tool for analyzing big data.
Another tool for big data analytics, which LexisNexis Risk Solutions maintains. As it has a single architecture to deliver one a single platform. It also has a unique data processing programming language to run. For effective implementation of big data tools, you can avail consulting services of providers like RemoteDBA.
Features of HPCC:
- A highly efficient big data tool that can accomplishbig data tasks with minimal code.
- Offers high availability and redundancy in the big data processing.
- Useful for complex processing of data on Thor clusters.
- An easy-to-use graphical IDE, which makes it much simpler to develop, test, and debug.
- It is optimized the code automatically for parallel and real-time processing of big data.
- Offers enhanced scalability and optimum performance.
- Code compilation into optimized C++.
Qubole is an autonomous platform for big data management. This is also an open-source tool, which is easily self-managed and can self-optimize by allowing the data teams to focus more on the actual business outcome.
Features of Qubole:
- A single platform for various use cases.
- Open-source software for big data having many engines optimized for cloud management.
- Comprehensive governance, security, and compliance.
- Offers actionable recommendations, alerts, insights.
- Ensures optimum performance, reliability, and affordable cost.
- Automatically enacts the policies to avoid doing repetitive tasks manually and automate the same.
Statwing is much easier to handle statistical tool. This is purpose-built for big data analysts. It is a very modern interface that can automatically choose statistical tests.
Features of Statwing
- An innovative big data software that explores any data in a matter of seconds
- It helps to clean the data, explore relationships, and also to create complicated charts in minutes.
- Allows creation of scatterplots, heatmaps, histograms, and bar diagrams which help export data to PowerPoint or Excel,
Apache Cassandra is a DB used popularly to ensure cost-effective management of huge volumes of enterprise data.
Cassandra features are:
- Support replication across various data centers and providing lower latency for the users.
- Automatic replicating data to various nodes for better fault tolerance and higher performance.
- One best big data tool that is highly suitable for those applications that cannot afford to lose their data even when the data center is down completely.
- Cassandra also offers dedicated service and support contracts offered by third-party providers.
The top features of CouchDB are:
- It is a single-node DB that works like any random database but with many add-on features.
- One of the top big data processing tools will let you run a single logical DB on many servers.
- CouchDB uses JSON data format and a universal HTTP protocol for processing.
- It enables easy replication of a DB across various server instances for increased fault tolerance.
- An easy and user-friendly interface for quick insertion of documents, updating, retrieving, and deleting data.
- The standard JSON-based format of CouchDB can be instantly transferred across various languages.
Pentaho offers many big data tools to help prepare, extract, and blend big volume data. It can also offer easy visualization and quick analytics, which can change the ways as to how the businesses are run. Big data tools offered by Pentaho can also turn big data into bigger insights for business decision making.
Key Pentaho features:
- Easy data access and quick integration for data visualization.
- Empowering the big data users to architect the big data right from the source and live stream it for real-time analytics
- Switching seamlessly or combining the data processing with cluster-based execution for optimum processing
- Allow checking data with quick and easy access to analytics with effective visualization using charts and reporting tools.
- Supports a wider spectrum of big data sources to offer some unique analytical capabilities
Along with these, based on your requirement, you may also explore more big data analytics tools, software, and databases like Flink, Cloudera, Openrefine, Rapidminer, DataCleanerm Kagglem Hive, etc., to name a few. All these will help you extract actionable information from huge data sets and process it according to your business objectives.