1) Hadoop
Apache Hadoop is a big data framework. It enables massive data sets to be processed across clusters of computers in a distributed manner. It is one of the best big data technologies available, with the ability to expand from a single server to thousands of machines.
Features:
- Authentication enhancements when using HTTP proxy server
- Specification for Hadoop Compatible Filesystem effort
- Support for POSIX-style filesystem extended characteristics
- Robust ecosystem suited to meet analytical needs of developers
- Flexibility in data processing
- Faster data processing
2) Atlas.ti
Atlas.ti is a comprehensive research tool. This big data analytic tool provides one-stop access to various platforms. It is used in academic, market, and user experience research for qualitative and mixed methodologies data analysis.
Features:
- Export information on each source of data
- Integrated way of working with data
- Allows renaming a code in the margin area
- Handles projects with thousands of documents and coded data segments
3) HPCC
HPCC (developed by LexisNexis Risk Solution) is a big data tool offering data processing services on a single platform, architecture, and programming language.
Features:
- Accomplishes big data tasks with far less code
- High redundancy and availability
- Supports complex data processing on a Thor cluster
- Graphical IDE simplifies development, testing, and debugging
- Automatic parallel processing optimization
- Enhanced scalability and performance
- ECL code compiles into optimized C++ and extends via C++ libraries
4) Storm
Storm is an open-source, real-time, fault-tolerant big data processing system.
Features:
- Benchmarked for processing 1 million 100-byte messages/sec per node
- Parallel calculations across cluster machines
- Auto-restarts workers if a node fails
- Guarantees each data unit is processed at least once or exactly once
- Easy to deploy and use for big data analysis
5) Qubole
Qubole is a self-contained platform for managing big data. It is self-managing and self-optimizing, enabling teams to focus on business goals.
Features:
- Single platform for all use cases
- Open-source engines optimized for the cloud
- Comprehensive security, governance, and compliance
- Actionable alerts, insights, and recommendations
- Automates repetitive manual actions
6) Cassandra
Apache Cassandra is widely used to manage enormous volumes of data effectively.
Features:
- Supports replication across multiple data centers
- Data automatically replicated to multiple nodes for fault tolerance
- Ideal for applications that can’t afford data loss
- Support contracts and third-party services available
7) Stats iQ
Qualtrics’ Stats iQ is a user-friendly statistical tool designed for big data analysts.
Features:
- Explores any data in seconds
- Cleans data, explores relationships, and creates charts in minutes
- Creates histograms, scatterplots, heatmaps, and bar charts exportable to Excel or PowerPoint
- Translates statistical results into plain English
8) CouchDB
CouchDB stores data in JSON documents, accessible via the web and JavaScript queries. It offers fault-tolerant storage and distributed scaling.
Features:
- Functions as a single-node database
- Uses HTTP protocol and JSON format
- Easily replicates databases across servers
- Simple interface for insert, update, retrieve, and delete
- JSON-based documents are language-translatable
9) Pentaho
Pentaho offers tools for extracting, preparing, and blending big data. It provides visual analytics to transform how businesses operate.
Features:
- Data access and integration for effective visualization
- Architect big data at source and stream for analytics
- Combine processing methods for maximum efficiency
- Easy access to analytics, charts, and reports
- Supports wide range of big data sources
10) Flink
Apache Flink is an open-source tool for stream processing large datasets.
Features:
- Accurate results even with out-of-order or late data
- Stateful and fault-tolerant with failure recovery
- High throughput and low latency
- Supports stream processing and event time semantics
- Flexible windowing based on time, count, or sessions
- Wide range of third-party connectors
11) Cloudera
Cloudera is a fast, secure, scalable big data platform allowing access to data from anywhere.
Features:
- High-performance analytics
- Multi-cloud support
- Manage Cloudera Enterprise on AWS, Azure, or GCP
- Pay-as-you-go cluster deployment
- Develop and train data models
- Real-time monitoring and insights
- Accurate model scoring and reporting
12) OpenRefine
OpenRefine is a powerful big data analytics tool for cleaning and transforming unstructured data.
Features:
- Explore large datasets easily
- Link and extend datasets via web services
- Import data in multiple formats
- Quick dataset exploration
- Basic and advanced cell transformations
- Handle cells with multiple values
- Instant dataset linking
- Named-entity extraction
- Use Refine Expression Language for advanced operations
13) RapidMiner
RapidMiner is an open-source platform for data preparation, machine learning, and model deployment.
Features:
- Multiple data management methods
- GUI or batch processing
- Integration with internal databases
- Shareable dashboards
- Predictive analytics for big data
- Remote analysis
- Data filtering, merging, joining, and aggregating
- Build, train, and validate models
- Stream data to databases
- Reports and notifications
14) DataCleaner
DataCleaner is a data quality and profiling tool that supports data transformation and cleansing.
Features:
- Fuzzy duplicate detection
- Data transformation and standardization
- Data validation and reporting
- Cleansing using reference data
- Hadoop data lake pipeline management
- Validates data rules before processing
15) Kaggle
Kaggle is the world’s largest big data community, ideal for sharing and analyzing open data.
Features:
- Discover and analyze open datasets
- Search for datasets with ease
- Participate in the open data movement
- Connect with data enthusiasts
16) Hive
Hive is a free and open-source big data solution built on top of Hadoop, allowing SQL-like querying.
Features:
- SQL-like query language support
- Uses mappers and reducers for query execution
- Supports task definition in Java or Python
- Designed for structured data only
- Abstracts complexity of MapReduce
- JDBC interface provided
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