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NoSQL databases play a critical role in handling Big Data due to their ability to manage large volumes, velocity, and variety of data. They provide scalable solutions for unstructured, semi-structured, and even structured data, unlike traditional relational databases that struggle with the high demands of Big Data environments.NoSQL databases have become integral to managing and leveraging Big Data effectively.Big Data refers to datasets that are so large or complex that traditional data processing applications are inadequate.NoSQL databases are designed to scale horizontally by adding more servers to distribute the load. This is crucial for Big Data applications that require handling vast amounts of data and high traffic.NoSQL systems often deliver high read and write throughput, making them suitable for real-time data processing and analytics. Different types of NoSQL databases and their respective strengths, organizations can effectively leverage Big Data to drive insights, innovation, and competitive advantage. However, it's essential to consider the specific requirements and challenges associated with NoSQL systems to ensure successful implementation and optimal performance.
Apache Cassandra is an open source NoSQL distributed database trusted by thousands of companies for scalability and high availability without compromising performance. Linear scalability and proven fault-tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data.
Development Language : Java
Tools : Apache NetBeans IDE 22
Operating System : Windows, Linux, OS X, BSD
Types : NoSQL Database, Data Store
Version: Apache Cassandra 5.0
Availability : Open Source
Area of Research : Non-relational database
Data Definition Operations (DDL): Operations that define or modify the schema, such as creating, altering, and dropping keyspaces and tables.
Data Manipulation Operations (DML): Operations that interact with the actual data, such as inserting, updating, deleting, and querying records.
Batch Operations: Allow executing multiple DML operations in a single transaction.
Consistency Management: Operations that allow tuning read/write consistency levels for fault tolerance.
Indexing and Searching: Operations that create or drop indexes to improve query performance.
• Rigid Architecture.
• Transaction SupportNoSQL Database illustration by using Bigdata.
• Easy Data Distribution.
• Flexible Data Storage.
• Fast Linear-scale Performance.
HBase is a distributed, scalable, NoSQL database built on top of the Hadoop Distributed File System (HDFS). It is designed to handle large volumes of data across a cluster of commodity hardware. HBase is particularly well-suited for real-time read/write access to large datasets, making it an integral part of big data analytics workflows.
Development Language : Java
Tools : Apache NetBeans IDE 22
Operating System : Windows, Linux, Unix
Types : Non-relational distributed database
Version: Apache HBase 3.3.1
Availability : Open Source
Area of Research : Non-relational database
Read Operation: HBase stores data in tables, where each table is organized by rows. Reading data involves accessing these rows using row keys.
Write Operation: HBase uses a Write-Ahead Log to ensure data durability. When a write operation is performed, the data is first written to the WAL, which logs changes before they are applied to the MemStore.
Memory Management: The MemStore holds recently written data in memory, providing fast access for read operations. It improves performance by reducing the need for disk I/O during frequent updates.
Multiple MemStores: THBase maintains a MemStore for each column family in a table, allowing for fine-grained control over memory usage and write performance.
Compaction: HBase periodically performs compaction to merge smaller HFiles into larger ones, optimizing storage and read performance.
• Horizontally scalable.
• Automatic Failover.
• Integrations with Map/Reduce framework.
• Multidimensional sorted map.
• Storing and Retrieving data.
MongoDB is a popular NoSQL database that stores data in a document-oriented format, which means it uses JSON-like documents instead of traditional relational database tables.
Development Language : C++
Tools : Apache NetBeans IDE 22
Operating System : Windows, Linux, OS, X, Solaris
Types : Document Oriented database
Version: MongoDB C++ Driver 3.8
Availability : Open Source
Area of Research : Non-relational database
Sharding (Horizontal Scaling): JIt is a method to distribute large datasets across multiple servers. MongoDB splits data into shards, where each shard is responsible for a subset of data.
Parallel Processing with MongoDB: MongoDB allows you to perform parallel queries across shards, distributing the load and improving query performance on large datasets.
Aggregation Framework: MongoDB’s aggregation framework allows you to process and analyze large data sets.
MapReduce: MongoDB supports MapReduce, which is ideal for large-scale data processing and complex transformations.
Bulk Write Operations: For handling big data, you can perform bulk write operations, which allow you to execute multiple inserts, updates, or deletes in one request, improving efficiency and performance.
• Parallel Processing.
• Support Multi-document ACID transactions.
• Supports various types of indexes.
• Easily adapts to changing requirements.
• Schema-less database.
Neo4j is a graph database management system that is designed to store, query, and manage highly connected data. Unlike traditional relational databases (SQL) that store data in tables, Neo4j uses a graph-based model where data is stored as nodes (representing entities) and relationships (representing connections between entities).
Development Language : Java, Scala
Tools : Apache NetBeans IDE 22
Operating System : Windows, Linux, OS, X, Solaris
Types : Graph Database
Version: Neo4j 5.23.0
Availability : Open Source
Area of Research : Non-relational database
Parallel Queries and Batch Operations: To handle large datasets, Neo4j offers parallel processing and batch operations.
Indexing for Large Data Sets: Indexing in Neo4j can drastically improve query performance, especially for big data operations.
Graph Projections for Efficient Data Loading: Projected graphs are an efficient way to handle large graphs without loading all data into memory at once.
Data Import and Bulk Loading: For handling large datasets, Neo4j provides efficient ways to import and bulk load data, especially when dealing with millions or billions of nodes and relationships.
Parallel Processing and Batch Operations: Neo4j allows for batch processing of large volumes of data, making it efficient for handling big data scenarios.
• Data model (flexible schema).
• ACID properties.
• Scalability and reliability.
• Cypher Query Language.
• Built-in web application.
CouchDB is an open-source NoSQL database designed for storing and managing JSON data. It provides a schema-free data model, allowing developers to store documents without predefined structures, making it highly flexible and suitable for various applications.
Development Language : Erlang
Tools : IntelliJ IDEA is 2024.2
Operating System : Windows, Linux, OS, X, Solaris, Android, BSD
Types : Document oriented NoSQL Database
Version: CouchDB 3.4.1
Availability : Open Source
Area of Research : Non-relational database
Bulk Inserts: CouchDB allows bulk document inserts, which is essential for importing large datasets efficiently. Using the _bulk_docs endpoint can help streamline this process.
Master-Master Replication: CouchDB supports multi-master replication, which is useful for distributing data across multiple nodes, especially in geographically distributed environments.
Continuous Replication: For ongoing big data projects, continuous replication ensures that data is synchronized in real-time, allowing for up-to-date datasets across different locations.
MapReduce Views: CouchDB employs MapReduce for querying data. You can create views using JavaScript to perform complex queries on large datasets.
Hadoop Integration: CouchDB can be integrated with Hadoop and Spark for big data processing. This allows you to leverage CouchDB's database features while performing large-scale data processing and analysis using these frameworks.
• Simplicity of design.
• Finer control over availability.
• Schema-Free Document Storage.
• Document Versioning.
• Offline Capability.
OrientDB is an open-source, multi-model NoSQL database management system that supports various data models, including document, object, and graph databases. This versatility allows it to handle complex data relationships while providing the flexibility of a document store.
Development Language : Java
Tools : Apache Netbeans IDE 22
Operating System : OS Independent
Types : Document oriented , Graph, Multi-Model Database, NoSQL Database
Version: OrientDB 3.2..34
Availability : Open Source
Area of Research : Non-relational database
Schema Management: OrientDB allows you to define schemas (both schema-less and schema-full), which helps in organizing data effectively.
CRUD Operations: Basic Create, Read, Update, and Delete operations are fundamental for managing data entries. OrientDB provides efficient APIs for performing these operations across large datasets.
Automatic Indexing: It can automatically index fields in documents and properties in graphs, optimizing access to frequently queried data.
Data Export Tools: OrientDB offers tools for exporting data to various formats for analysis in other systems or for backup purposes, ensuring flexibility in data management.
Graph Traversals: You can perform graph traversals to explore relationships within your data, which is particularly useful for social network analysis and recommendation systems.
• Multi-Model Database.
• ACID Compliance.
• SQL-Like Query Language.
• Graph Processing Capabilities.
• Replication and Clustering.
Terrastore is an open-source distributed NoSQL database designed for storing and managing large amounts of data in big data applications.
Development Language : Java
Tools : Apache NetBeans IDE 22
Operating System : OS Independent
Types : Document Oriented Database
Version: Terrastore 1.2.0
Availability : Open Source
Area of Research : Non-relational database
The data layer: Documents are partitioned and distributed among your nodes, with automatic and transparent re-balancing when nodes join and leave.
The Computational Layer: Query and update operations are distributed to the nodes which actually holds the queried/updated data, minimizing network traffic and spreading computational load.
Create, Read, Update, Delete: Terrastore supports basic CRUD operations, enabling users to manage data entries easily.
RESTful API Queries: Terrastore provides a RESTful API for querying data, allowing users to retrieve information using standard HTTP methods(GET, POST, PUT, DELETE).
Partitioning: Data partitioning helps distribute data across different nodes, enhancing performance and scalability for large datasets.
• Server Side Update function.
• Push-down predicates.
• No impedance mismatch.
• Ubiquitous.
• Range Queries.
FlockDB is a distributed graph database developed by Twitter specifically for storing and managing large-scale graphs, primarily focusing on handling relationships between entities.
Development Language : Java
Tools : Apache NetBeans IDE 22
Operating System : OS Independent
Types : Graph Database
Version: FlockDB 1.6.0
Availability : Open Source
Area of Research : Non-relational database
Adding Vertices and Edges: FlockDB allows you to efficiently add vertices (nodes) and edges (relationships) to the graph.
Querying Relationships: FlockDB provides APIs for querying relationships between vertices. The retrieval of all edges connected to a particular vertex or find specific relationships based on various criteria.
Graph Traversal: The performance of graph traversal operations to explore relationships, which is essential for applications like social networks or recommendation systems.
Modifying Vertices and Edges: The updation properties of vertices and edges as needed, allowing for dynamic changes in the graph structure to reflect real-world updates.
Data Export/Import: FlockDB can be integrated with other data processing frameworks, allowing for the export and import of graph data to and from various systems.
• Distributed Architecture.
• Batch Processing Capabilities.
• Simple API.
• Focus on Relationships.
• Fault Tolerance and High Availability.
Hibari is an open-source distributed NoSQL database designed for managing large volumes of data in a scalable and efficient manner.
Development Language : Java, Python, C++, Erlang
Tools : Apache Netbeans IDE 22, Spyder 6.0.1, IntelliJ IDEA 2024.2
Operating System : OS Independent
Types : No-SQl Database
Version: Hibari 1.1.0
Availability : Open Source
Area of Research : Non-relational database
Data Adding Opeartion: Hibari allows for the addition of key-value pairs to the database. This operation is fundamental for populating the database with data from various sources.
Data Querying Opeartion: Hibari provides APIs to retrieve values based on their associated keys. This operation enables applications to access data quickly and efficiently.
Records Modifying Opeartion: Hibari supports the modification of existing key-value pairs. You can update the value associated with a specific key as needed.
Records Removing Opeartion : Hibari enables the deletion of key-value pairs from the database. This operation is important for maintaining data accuracy and relevance.
Cascade Deletion: You can implement cascade deletion to ensure related data is also removed when a key is deleted, maintaining data integrity.
• Key-Value Store Model.
• Consistency and Partition Tolerance.
• Support for Various Data Types.
• High Bandwidth.
• Custom Metadata.
Riak is an open-source distributed NoSQL database designed for high availability, fault tolerance, and scalability. It is particularly well-suited for big data applications due to its unique features and architecture.
Development Language : Erlang
Tools : IntelliJ IDEA 2024.2
Operating System : OS Independent
Types : No-SQl Database
Version: Riak 2.9.0
Availability : Open Source
Area of Research : Non-relational database
Apache Spark Connector: The Apache Spark Connector for Riak automatically synchronizes data between Spark and Riak. This combines the in-memory analytics of Apache Spark with the resiliency and scale of Riak.
Redis Cache Integration: It improves application performance by reducing latency and includes built-in cluster management, high availability, automatic data sharding, and the ability to replicate and sync data between Riak Key-Value and Redis.
Apache Solr Integration: Get the powerful full-text search of Apache Solr with the availability and scalability of Riak KV. As data changes, search indexes are automatically synchronized and integrated search makes it easy to query Riak Key-Value data sets using Apache Solr.
Apache Mesos Integration: The Riak Mesos Framework enables the provisioning and management of large Riak clusters deployed with Apache Mesos allowing better resource utilization in your clusters.
RIAK S2: Riak S2 is a highly available, scalable, easy-to-operate object storage software solution that’s optimized for holding videos, images, and other files. It provides simple but powerful storage for large objects built for private, public, and hybrid clouds.
• Predictable Latency.
• Tunable consistancy.
• Operational Simplicity.
• Storage Options.
• Fault Tolerance.
Hypertable is an open-source, distributed database designed for high performance and scalability, built on top of Google's Bigtable architecture. It is particularly well-suited for handling large amounts of structured data in big data environments.
Development Language : C++
Tools : Apache Netbeans IDE 22
Operating System : OS X, Linux, Windows
Types : No-SQl Database
Version: Hypertable 0.9.9
Availability : Open Source
Area of Research : Non-relational database
Hyperspace: Hyperspace is a highly available lock manager and provides a filesystem for storing small amounts of metadata.
Master: The master handles all meta operations such as creating and deleting tables. Client data does not move through the Master, so the Master can be down for short periods of time without clients being aware.
Range Server: Range servers are responsible for managing ranges of table data, handling all reading and writing of data.
FS Broker: Hypertable is capable of running on top of any filesystem. To achieve this, the system has abstracted the interface to the filesystem by sending all filesystem requests through a File System (FS) broker process.
ThriftBroker: Provides an interface for applications written in any high-level language to communicate with Hypertable.
• Column-Family Data Model.
• Automatic Data Sharding.
• Compatibility with HDFS.
• Extensibility and Customization.
• SQL-like Query Interface.
Hive is a data warehouse infrastructure tool to process structured data in Hadoop. It resides on top of Hadoop to summarize Big Data, and makes querying and analyzing easy.
Development Language : Java
Tools : Apache NetBeans IDE 22
Operating System : OS Independent
Types : Relational Database
Version: Hive 3.2.4
Availability : Open Source
Area of Research : Non-relational database
Execute Query: The Hive interface such as Command Line or Web UI sends query to Driver (any database driver such as JDBC, ODBC, etc.) to execute.
Get Plan: The driver takes the help of query compiler that parses the query to check the syntax and query plan or the requirement of query.
Get MetaData: The compiler sends metadata request to Metastore (any database).
Send MetaData: Metastore sends metadata as a response to the compiler.
Send Plan: The compiler checks the requirement and resends the plan to the driver.
Execute Plan: The driver sends the execute plan to the execution engine.
Executer Task: The execution engine sends the job to JobTracker, which is in Name node and it assigns this job to TaskTracker, which is in Data node.
Fetch Results: The execution engine receives the results from Data nodes.
Send Results: The execution engine sends those resultant values to the driver. And also The driver sends the results to Hive Interfaces.
• SQL-like query language.
• Schema-on-read.
• Partitioning.
• Bucketing.
• Extensibility.
Infobright is a specialized data management solution designed for analytics and big data applications, particularly known for its columnar storage architecture.
Development Language : C
Tools : Apache NetBeans IDE 22
Operating System : Linux, Windows
Types : Column-Oriented Relational Database
Version: InfoBright 4.0
Availability : Open Source
Area of Research : Non-relational database
Ad Hoc Querying: With Infobright, users can execute ad hoc queries without requiring the creation of indexes or additional database tuning.
Data Loading (ETL): Infobright is optimized for fast ETL (Extract, Transform, Load) processes. It allows for efficient bulk loading of data and supports integration with various big data ecosystems and ETL tools.
Query Optimization: Infobright is designed for analytic queries, not transactional workloads. It focuses on read-intensive operations and is capable of handling complex aggregate queries across large datasets.
Columnar Storage: Data is stored in a columnar format, which optimizes performance for analytic queries by reading only the necessary columns rather than entire rows.
Data Compression: It offers extreme data compression (up to 10x-40x), which reduces the storage requirements and speeds up query execution.
• Columnar Storage Format.
• High Data Compression.
• Knowledge Grid for Query Optimization.
• No Need for Indexing or Partitioning.
• Parallel Processing for Large Queries.
Infinispan is an open-source distributed in-memory data grid and key-value store designed to manage large volumes of data across clusters of machines.
Development Language : Java
Tools : Apache NetBeans IDE 22
Operating System : OS Independent
Types : No-SQL Database Store
Version: Infinispan 14.0.14
Availability : Open Source
Area of Research : Non-relational database
In-Memory Caching: Infinispan serves as a distributed in-memory cache, speeding up data access for applications by reducing latency.
Cache Synchronization: Data can be synchronized across multiple nodes to ensure consistency and availability, which is crucial for big data environments where data is continuously changing.
Advanced Query Language (JPQL): Infinispan supports querying capabilities using a SQL-like syntax, allowing for complex queries across distributed datasets. This is particularly useful for analytics in big data applications.
Full-Text Search: Integration with Apache Lucene enables full-text search capabilities, allowing users to perform advanced search operations on data stored in Infinispan.
Put/Get Operations: Infinispan allows you to store and retrieve data as key-value pairs, supporting both synchronous and asynchronous methods. This is useful for managing large datasets efficiently.
• Pluggable architecture.
• Support LRU(Least Recently Used ) algorithm.
• Support LRI (Least Recently Inserted)Seviction algorithm.
• MapReduce.
• Transaction.
Redis is an open-source, in-memory data structure store widely used as a database, cache, and message broker. It is particularly effective in big data applications due to its speed and flexibility.
Development Language : C
Tools : Apache NetBeans IDE 22
Operating System : BSD, Linux, OS X, Windows
Types : Key-Value Database
Version: Redis 7.0.12
Availability : Open Source
Area of Research : Non-relational database
CRUD Operations: Redis allows users to perform basic CRUD operations on its various data types (strings, lists, sets, hashes, etc.).
Atomic Operations: Redis supports atomic operations on data types, ensuring that operations on data structures can be completed without interference from other commands.
Pub/Sub Messaging: Redis provides a publish/subscribe messaging pattern, allowing messages to be sent to multiple subscribers.
Geospatial Operations: Redis supports geospatial indexing and querying, allowing users to store and query geographic data.
Backup and Persistence: Redis offers persistence options, including RDB (snapshotting) and AOF (append-only file). These features enable data recovery and backup operations, ensuring durability in big data environments.
• Versatile Data Structures.
• High Availability and Scalability.
• Pub/Sub Messaging.
• Lightweight and Fast.
• Atomic Operations and Transactions.