Data forms a major portion of today’s business world. Unfortunately, sifting through the chunks of data is not a menial task.
Thankfully, there are a variety of big data tools that extract useful information efficiently.
Companies all over the world rely on these sets of data to get a leg up on their competitors. Big data tools are a means of transforming the unstructured, unorganized data sets into structured and organized information . The big data tools separate useful knowledge from the rest and make it easy to gather the required information.
The massive data sets are hard to analyze, mining the right information from such large data sets is a monstrous task. This is where big data analytics tools come in. Since there are hundreds of big data tools available in the market, it can be hard to find one that suits your preferences.
Luckily, we have decided to take up this heinous task and ease your troubles. This article lists top big data tools and their key features so, you can just read through and pick and choose the one that suits you best. But before we get to that, let us understand what big data means and what are big data tools.
What Is Big Data?
Big data refers to large and complex sets of data. The size of a single data set can range from a few terabytes to hundreds of petabytes. Data sets, this enormous, are extremely hard to manage with traditional tools.
Since it’s almost impossible to handle these data sets via the usual methods, companies look for better options that can help them efficiently manage the data sets and extract the required information in relatively less time.
Big Data Tools To The Rescue!
What are big data tools? Big data tools are analytics tools that help analyze and process big data. These tools are designed for the cost-efficient management of data with better time management.
These tools allow companies to piece together important chunks of information. Through the information obtained from these big data analytics tools, companies identify market trends and patterns, create profitable strategies, and take better decisions for the success of their business.
Now that we are familiar with the answer to ‘ what are big data tools’, let us dive into the world of top big data tools available in the market.
List Of Top 10 Best Big Data Analytics Tools
Following is the list of the best open-source big data tools:
1. Hadoop
Hadoop is one of the best open-source data analytics tools. Hadoop is capable of handling all kinds of data. It possesses the ability to store huge amounts of data, handle massive data sets, and process these data chunks.
Hadoop utilizes simple programming models to process large-scale data sets and distribute them across various computer clusters. This means that you can scale up from single servers to thousands of machines.
Though you need to be familiar with Java to utilize the full expanse of Hadoop’s features. But it is worth the effort since it helps in exploring data, filtering, sampling, and summarization.
Key Features:
- Consists of four parts: Hadoop Distributed File System (HDFS), MapReduce, YARN, and libraries
- HDFS is capable of holding all types of data sets such as videos , images, etc.
- Useful for both R&D and production.
- Makes data processing flexible
- Processes data faster and helps efficiently manage time
- Scalable
- Allows quick access to data
2. Apache Storm
Apache Storm is one of the most reliable tools used in big data. It is an open-source distributed real-time computation system. It offers real-time processing of an unlimited stream of data.
Key Features:
- Offers massive scalability
- Can process one million 100 byte messages per second per node
- Automatically restarts in case of the death of a node
- Guarantees the processing of each unit of data at least once
- Fault-tolerant
- Can be used with any programming language
- Simple and fun to use
- Multiple-use cases including real-time analytics, online machine learning, continuous computation, distributed RPC, ETL, etc.
- Integrates with already available queuing and database technologies.
3. Cassandra
Cassandra is a free data analytics tool. It efficiently manages huge chunks of data across multiple servers. It is one of the best tools used in big data offering fault tolerance and high performance with no points of failure.
It was developed by Facebook as a NoSQL solution and is not used by industry giants such as Twitter, Netflix, Cisco, etc.
Key Features:
- Fault-tolerant
- No points of failure and no bottlenecks
- Highly scalable
- Extremely durable and reliable
- Data automatically replicated to multiple nodes
- Able to handle a massive amount of data
- Built-in security features
- Cassandra’s support contracts and services are available from third parties.
4. HPCC
HPCC is short for High-Performance Computing Cluster. HPCC is an easy-to-learn big data tool. With HPCC, you can acquire, enrich, deliver, and curate information faster hence saving your time and money.
It offers complete data lake management from ingestion and enrichment to content delivery. Its lightweight core architecture provides high performance.
Key Features:
- Open-source data lake platform
- High redundancy and availability
- Automatically optimizes code for parallel processing
- Batch, real-time, and streaming data ingestion
- Built-in data enhancement and Machine Learning APIs
- Scalable to many petabytes of data
- Fast and powerful
- Runs on commodity hardware and in the cloud
- Increased responsiveness to customers and stakeholders
5. OpenRefine
OpenRefine is a big data analytics tool used to process messy data. It cleans, transforms, extends, and improves it. It can process huge unstructured sets of data easily and quickly.
It provides foolproof security unless or until you authorize the exchange of data. It also allows extensions with web services and external data.
Key Features:
- Helps you easily explore large sets of data
- Has the ability to process large sets of data in seconds
- Can be used to link and extend your dataset with various web services
- Data can be imported in various formats
- Ability to handle cells with multiple cell values
- Refine Expression Language can be used to perform advanced data operations
6. RapidMiner
RapidMiner is a fully transparent, end-to-end data science platform. It is used for data prep, machine learning, and model operations. Helps you turn data into insights for better decision-making. The world’s top businesses such as Hitachi, Samsung, BMW, etc. use RapidMiner for data processing and mining.
Key Features:
- Provides a GUI to design and execute workflows
- Allows the use of multiple data management tools
- Offers good customer support and technical services
- Is capable of modeling, analyzing, and manipulating data
- Improve efficiency
- Offers remote analysis processing
- Real-time scoring
- Enterprise scalability
- Graphical user interface
- One-click deployment
7. MongoDB
MongoDB is an open-source distributed database built for modern application developers and the cloud era. it utilizes documents and collections to store large volumes of data. It is ideal for businesses that need quick decision-making.
It is written in Java, C++, and C. It is especially efficient for data sets that change frequently or the ones that are unstructured or semi-structured. It stores data from product catalogs, mobile applications, content management systems, etc.
Key Features:
- Has indexing and querying capabilities
- Easy to use
- High scalability
- Offers multi-cloud data distribution
- Safe and secure for sensitive data
- Allows you to manipulate, visualize, and analyze your data
- Reliable for mission-critical workloads
- Build for optimal performance
- Provides operational efficiency
8. Cloudera
Cloudera is one of the fastest, easiest, and most secure big data processing tool. It allows you to get data from any environment. To help your team members have better access to the stored data by building an enterprise data hub.
It helps in the collection, administration, management, processing, and distribution of unlimited volumes of data. It also offers data security to make sure that your data is safe and secure.
Key Features:
- High performing data analytics tool
- Real-time insights for data monitoring and detection
- Can be deployed across various cloud platforms
- Allows you to spin or terminate data clusters
- Can develop and train data models
- Helps conduct accurate model scoring and serving
9. Qubole
Qubole is an open-source, simple, and secure data lake platform that learns, and evolves from your usage. It offers enterprise-level solutions. It is one of the most flexible programs and is also easily accessible.
It is ideal for machine learning, streaming, and ad hoc analytics. Provides end-to-end services to enhance efficiency and effectively reduce the amount of time and effort sent on data analysis.
Key Features:
- Open, simple, and secure
- Fast adoption at scale
- Near zero administration
- Reduces cost by 50%
- Automation helps avoid performing repetitive manual actions
- Actionable insights, recommendations, and alerts make it more reliable
- Offers comprehensive security, governance, and compliance
10. Neo4j
Neo4j is the last data analytics tool in our list of 10 best tools used in big data. It is widely used in the graph database in the big data industry. It supports .Net, Java, JavaScript, Python and Go.
Neo4j offers a unique connections first technique to optimize the mapping, analysis, and storage of complex data. It also traverses networks to unearth invisible contexts and hidden relationships.
Key Features:
- Supports ACID transaction
- Highly scalable and reliable
- Offers flexibility
- Can integrate with other databases
- Supports Cypher i.e. query language for graphs
- Has a built-in Neo4j browser web application
Conclusion
The above-mentioned top big data tools can be used by companies to understand their target audience, predict market trends, and develop better strategies for the future.
You need to carefully analyze the key features of each tool and then pick the one that fulfills your requirements and under your budget.
Good Luck!
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