Top 20 Big Data Tools 2019

Introduction

The growing nature of data across the world become unstoppable and are immutable, hence it creates a lot of unexpected complexities in managing the data with secured hands 90% of data which comes from different sources are virtual and are heavy in size. So to process this large amount, we have to classify it first. Big data industry is using 3V methodology, to classify the data and to process the data. The 3V’s are, Volume - that determines the size of data sets, velocity - which determines the speed of data generation, and the Variety - which determines the type of data to declare whether it is structured or unstructured data. So any data which accomplish any of this classification is referred to big data. So, to facilitate the data processing and data analysation of data which comes in different nature, the data science industry often welcomes new arrival of tools that could ease the data processing.

In this article we gonna discuss the top trending 20 big data tools of 2019 that would best suit for your company, we have prepared this list of tools by keeping cost efficiency and time management as first priority.

Apache Hadoop

It is a library framework that allows us to proceed distributed processing of large data sets across various cluster of computers. It can be scaled up to handle thousands of server machines. It can detect the failures and handle them at the application layer.

Features

1. Users can easily write and test on distributed systems.

2. It automatically distribute the data across the machines and can utilize the parallelism of CPU core.

3. It doesn’t rely on hardware to provide fault - tolerance

4. Can add or remove clusters dynamically.

5. Compatible with all platforms

Apache Spark

By the definition, it is a fast, open source, general purpose cluster computing framework. API’ can be developed in JAVA, Scala, R and python languages. This framework supports to process large sets of data across various clusters of computers. It can be scaled up to manage and support single servers to large server machines.

Spark can cover large amount of work loads like interactive queries, streaming, batch applications, algorithm iteratives and more. It can reduce the burden of managing multiple tools.

Features

Speed: it helps to run an application on hadoop cluster, 100 times faster in memory & 10 times faster while running on disk. This will be possible by reducing the read/write operations on disk.

● Multiple Programming Language Support

Spark can provide built in API’s In different languages like JAVA, Scala, Python, and more. So therefore it allows us to write applications in different languages.

● Supports Advanced Analytics

It supports SQL queries, Machine Learning , Graph algorithms and streaming data

Apache Storm

It is an open source real time big data computation system and also free to use. It can process unbounded streams of data in a distributed real time.

Features

1. Can process one million 100 bytes messages per second

2. Uses parallel calculations

3. Can restart automatically whenever a node dies inside the cluster.

4. Guaranteed processing of each unit of data at only one time.

5. Scalable, fault-tolerant, easy to setup and operate.

Tableau

Table is the powerful tool ever, it helps to simplify the raw data into an easily understandable data sets. Tableau work nature can be easily understandable by professionals who are in any level of an organization. It connects and extract the data from various sources.

Features

1. Data blending possible

2. Real time analysis can be done

3. Data collaboration can be accomplished.

Apache Cassandra

Effective management of large set of data can be done by apache cassandra, without compromising the performance it can provide you scalability and high ability. Cassandra is fault tolerant, decentralized, Scalable, High performer.

1. Supports replicating across various and multiple data centers

2. To avoid fault tolerance data can be automatically replicated to multiple nodes.

3. Highly suitable for applications that don’t want to lose the data.

Flink

It is also an another open source, distributed Big data tool that can stream process the data with no hassles.

Features

1. Provide accurate results for out of order and delayed data

2. Can easily recover from failures

3. Can run on thousands of nodes

4. Having high throughput, and latency

Cloudera

Faster, easier and highly secure modern big data platform. It allows user to get data from any environment within a single and scalable platform.

Features :

1. Unbelievable performance analytics

2. Multi cloud provision

3. Can manage cloudera enterprise across AWS.

4. Delivers real time insights

5. Terminates cluster

HPCC

Developed by LexisNexis Risk Solution. It delivers data processing on a single platform with a single programming language support.

Features

1. Accomplishes big data analyzation of task with less amount of code.

2. High redundancy

3. Can be used for complex data processing

4. Simplifies Development, testing and debugging with Graphical IDE support

5. Enhanced scalability and Performance

Qubole

It is an autonomous big data platform. Wll be self managed, self- optimized, it allows businesses to focus on better outcomes.

1. Runs under single platform for any kind of use cases

2. Open source, and can be optimized for cloud operations

3. Provides real time actionable alerts and notification to optimize the perfomance

Statwing

It is an easy to use big data tool, that focuses on statistical reports.

Features

1. Explores data in seconds

2. It helps to cleanse the data and create charts in seconds

3. We can create histograms, heatmaps, and bar charts at any time

CouchDB

It is the only big data tool that stores data in JSON Documents, It provides distributed scaling with ultra fault tolerant. It allows data accessing through couch replication tool.

1. It is a single node database.

2. Runs on any number of servers.

3. Easy interface for inserting documents, updating and retrieving them

4. Stored JSON documents can be translatable in various languages.

Pentaho

This big data tool can be used to extract, prepare and blend the data. It provides both visualization and analytics for a business.

1. Architects data in source and can stream them for accurate analytics

2. Can combine data processing seamlessly within clusters in order to bring maximum process output.

3. Allow easy access to analyse data with in depth data charts, visualizations and reportings.

Openrefine

Openrefine is also another big data tool , it can help us to work with a large amount of messy data.

Features:

1. It helps to explore large data sets with easy manner

2. Can Link and extend data set across various web services.

3. Take just milliseconds to explore datasets.

4. Make instantaneous link between data sets.

Rapidminer

It is also an another open source big data tool. Which is used for data prep, machine learning, and data model deployments.

Features:

1. Can allow multiple data management methods.

2. Uses GUI for data processing.

3. Generates interactive and shareable dashboards

4. Processing based on Remote analysis.

Data Cleaner

It is a Data quality analysis tool, inside the data cleaner there is a strong data profiling technique.

Features :

1. Interactive and explorative data profiling feature.

2. Detects fuzzy records

3. Validates data and reports them

4. Use of reference to clean the data

Kaggle

It is a big data community , were businesses, organizations and researchers can analyze their data seamlessly.

Features :

1. Can discover and analyze open data.

2. Searchboxes to find open data sets.

Hive

It is an open source software big data tool. Can help to analyze large data set on hadoop. Querying and managing large data sets at real fast.

1. It supports SQL for Data Modelling

2. Allows defining the tasks using JAVA or python.

3. It is designed only for managing and querying structured data.

Kafka

It is a community, capable of handling trillions of events a day. Created in 2011 and open sourced by linkedin.Initially this was started as a messaging platform then within a short period it has been diverged in to even streaming platforms, It maintains in top with fast performance even when there is occurrence of Datas in TB.

Features

● Reliability, Scalability, Durability, Performance

Graph databases

It is a NoSQL Database uses graph data model comprised of different vertices to represent relationships between nodes .

Features :

● Highlights the links and relationship between various data.

Elasticsearch

It is a search based lucene library, distributed, full-text search engine with an HTTP web interface.

Feature:

1. It is compatible on every platform.

2. Real time, within a second of adding the document it can searchable inside the search engine.

3. Elastic search made it easy to handle multi-tenancy

Hope we have covered about a short intro on major big data tools, that are trend this year 2019.

Who we are ?

Bibrainia - A big data solutions company, providing best outputs and data analyzation services in big data industry with strong customer base. We prefer best tools to analyze the data, that best suits your organization. Our consultants and big data analysts are expertize in handling all the above tool

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