Introduction
The growing nature of data across the world become unstoppable and immutable, hence it creates a lot of unexpected complexities in managing the data with secured hands 90% of data that comes from different sources are virtual and are heavy in size. So to process this large amount, we have to classify it first. The big data industry is using 3V methodology, to classify the data and process the data. The 3V’s are Volume – which determines the size of data sets, Velocity – which determines the speed of data generation, and Variety – which determines the type of data to declare whether it is structured or unstructured data. So any data which accomplishes any of this classification is referred to as big data. So, to facilitate data processing and data analyzation of data which comes in different natures, the data science industry often welcomes new arrival of tools that could ease data processing.
In this article we gonna discuss the top trending 20 big data tools of 2019 that would best suit your company, we have prepared this list of tools by keeping cost efficiency and time management as first priority.
1. Apache Hadoop
It is a library framework that allows us to proceed distributed processing of large data sets across various clusters of computers. It can be scaled up to handle thousands of server machines. It can detect failures and handle them at the application layer.
Features
1. Users can easily write and test on distributed systems.
2. It automatically distributes the data across the machines and can utilize the parallelism of the 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.
2. Apache Spark
By 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 a large number of workloads like interactive queries, streaming, batch applications, algorithm iterative 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 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
3. Apache Storm
It is an open-source real-time big data computation system and is also free to use. It can process unbounded streams of data in distributed real-time.
Features
1. Can process one million 100 bytes of 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 set up and operate.
4. Tableau
Tableau is the most powerful tool ever, it helps to simplify the raw data into easily understandable data sets. Tableau’s work nature can be easily understandable by professionals who are at any level of an organization. It connects and extracts the data from various sources.
Features
1. Data blending possible.
2. Real-time analysis can be done.
3. Data collaboration can be accomplished.
5. Apache Cassandra
Effective management of large sets 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.
Features
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.
6. Flink
It is also 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.
7. Cloudera
Faster, easier and highly secure modern big data platform. It allows users 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.
8. 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 analysis of tasks 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.
9. Qubole
It is an autonomous big data platform. Will be self-managed, self- optimized, and it allows businesses to focus on better outcomes.
1. Runs under a 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 notifications to optimize performance.
10. 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.
11. CouchDB
It is the only big data tool that stores data in JSON Documents, It provides distributed scaling with ultra fault tolerance. It allows data access through the 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.
12. 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 analyze data with in-depth data charts, visualizations and reporting.
13. 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 in an easy manner.
2. Can Link and extend data set across various web services.
3. Take just milliseconds to explore datasets.
4. Make instantaneous links between data sets.
14. Rapidminer
It is also 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.
15. 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
16. Kaggle
It is a big data community, where businesses, organizations and researchers can analyze their data seamlessly.
Features
1. Can discover and analyze open data.
2. Search boxes to find open data sets.
17. Hive
It is an open-source software big data tool. Can help to analyze large data sets on Hadoop. Querying and managing large data sets at real fast.
Features
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.
18. 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 into even streaming platforms, It maintains on top with fast performance even when there is the occurrence of Datas in TB.
Features
● Reliability, Scalability, Durability, Performance
19. Graph databases
It is a NoSQL Database that uses a graph data model comprised of different vertices to represent relationships between nodes.
Features :
● Highlights the links and relationships between various data.
20. Elasticsearch
It is a search-based Lucene library, distributed, full-text search engine with an HTTP web interface.
Feature:
1. It is compatible with 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 a short intro on major big data tools, that are trend this year 2019.
Who we are?
Bibrainia – A big data solutions company, providing the best outputs and data analysis services in the big data industry with a strong customer base. We prefer the best tools to analyze the data, that best suits your organization. Our consultants and big data analysts are expertized in handling all the above tools.