Let’s assume you run a business enterprise and it has major data sources that generate real-time information about the users. Choosing a right big data tool for your enterprise is an important step because once you begin with the project, it is extremely cumbersome and resource-intensive to shift from one solution to another. In today’s post, we have compiled the top 5 big data tools, along with their list of significant features, which can be used by an enterprise to computationally analysis the data and reveal clever insights. Let’s have a look.
1. Apache Hadoop
Apache Hadoop is one of the most prevalent big data tools for accurately analyzing large data volumes. It is basically an open-source, Java-based programming framework that allows for the processing and storage of large datasets with the help of simple programming models. Hadoop is extensively being used for many big data processing jobs, including statistical analysis, sales planning as well as processing the colossal sums of data generated by the IoT sensors.
- Hadoop is an open-source framework which runs on low-cost commodity hardware.
- It deploys MapReduce programming model that enables it to process, manage and store data at a petabyte scale, where petabytes of data can be processed in a couple of hours.
- Large computing clusters are often prone to failures. However, Hadoop is highly reliable. For instance, if a node in the cluster fails, the data processing is automatically re-directed to the remaining nodes and the data is re-replicated in order to combat any future node failures.
- With Hadoop, you can store your data in any format, may it be structured or unstructured. And later, when the data has to be read, you can apply any structured schemas to it.
Apache Cassandra is a distributed NoSQL database management system that allows managing large volumes of data located across a number of commodity servers. This is a highly effective tool that manages several nodes simultaneously, leaving no single point of failure. Due to its high availability, ease of operation, hassle-free data distribution and a great ability to scale, some of the biggest data enterprises such as Instagram, eBay, GoDaddy, Netflix, and Apple use Cassandra for executing real-time analytics.
- Cassandra is an open source framework which comes with an extensive community where a number of people share their views related to Big Data. Also, Cassandra can be easily integrated with Apache’s other projects such as Hadoop, Apache Hive, etc.
- In Cassandra, the data is automatically duplicated to multiple nodes to avoid failures. Also, failed nodes can be replaced with new nodes without having to take it down, thus no downtime.
- It is decentralized. Every node in the computing cluster is identical. Thus, there is no single point of failure.
- Cassandra is suitable for enterprises that cannot afford to lose a single piece of data, even in case of failure of the entire data center.
- Cassandra comes with a rich data model which is largely column oriented. All the data is stored in columns, unlike traditional databases that make the storage effectual and well-organized.
The best part about elastic search is that it is highly flexible, i.e. it allows the user to obtain data from any source, in any format and in any quantity and analysis it to reveal valuable insights. It is horizontally scalable and is known for its reliability and ease of management.
- In contrast to traditional databases that usually take more than 10 seconds to fetch results, Elasticsearch returns results in under 10 milliseconds on the same hardware.
- Combining the speed of search with the potential of detailed analysis, Elasticsearch observes a developer-friendly JSON style query language that works perfectly for both structured and unstructured data.
- Elasticsearch features a distributed architecture. It can scale up to hundreds of servers and store petabytes of data.
KNIME, also known as Konstanz Information Miner, is an open source data analytics tool that allows you to discover the amazing potential hidden in your data and source clever insights. Easy to deploy and scale, it incorporates various components for machine learning and data analyzing through its modular data pipelining concept.
- Every node in the clusters stores all the data permanently. Therefore, the workflow in the execution can be stopped at any node and easily resumed later on.
- KNIME allows additional plugins that enable the integration of various methods for text mining and image mining.
Big Data holds immense potential in terms of deriving real-time acumens about your user’s behavior. It can help you propel your business in the right direction by obtaining significant information about your user needs and preferences. These above mentioned open source Big Data tools can certainly help you remove the difficulty of managing colossal volumes of data. However, you need to understand these in detail so as to know the right fit for your business analytic needs.