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There haven't been specific details publicly disclosed about the extensive use of Big Data technologies at the Indian Space Research Organisation (ISRO). However, it's worth noting that space agencies, including ISRO, often deal with massive volumes of data generated by satellites, space missions, and other projects.

 

Given the nature of their work, it is plausible that ISRO may leverage Big Data technologies for various purposes, such as data storage, processing, analysis, and extracting valuable insights from the vast amounts of data collected through satellite imagery, telemetry, and scientific instruments.

 

The use of Big Data technologies in space exploration and satellite data analysis has become increasingly common across various space agencies globally. These technologies can facilitate the handling of large datasets, enable real-time processing, and support complex analytics tasks.

 

For the most accurate and up-to-date information regarding ISRO's use of Big Data technologies, it is recommended to check official announcements from ISRO, publications, or contact the organization directly. Organizations like ISRO may not always disclose specific details about their internal technology implementations for security or strategic reasons.

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For cloud services : - AWS : DynamoDB - AZURE : CosmosDB which have APIs for Cassandra, MongoDB. NoSQL databases like MongoDB, Cassandra, Neo4j, and Redis are often used for big data analytics in a variety of applications and industries due to their flexibility, scalability, and performance.

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Comparing R, a statistical programming language, with Cloudera, a big data platform, is like comparing apples and oranges. They serve different purposes within the realm of big data analytics: 1. R: R is a popular programming language and environment for statistical computing and graphics. It's widely used for data analysis, statistical modeling, and visualization. R provides a vast array of statistical and graphical techniques and is highly extensible through packages contributed by the community. 2. Cloudera: Cloudera is a big data platform that provides a suite of tools and services for storing, processing, and analyzing large volumes of data. It is built on top of open-source technologies such as Apache Hadoop, Apache Spark, and others. Cloudera offers solutions for data storage (Hadoop Distributed File System - HDFS), data processing (Apache Spark, Apache Hive, etc.), data management, security, and governance. While R is primarily used for statistical analysis and modeling on smaller datasets that can fit into memory, Cloudera is designed to handle massive datasets distributed across clusters of computers. Cloudera enables parallel processing and distributed computing, allowing users to analyze and derive insights from petabytes of data. That said, R can be integrated with Cloudera's platform. Cloudera provides tools and connectors that allow users to run R scripts on their data stored within the Cloudera ecosystem, leveraging the scalability and processing power of the platform for large-scale analytics. In summary, R and Cloudera serve different but complementary roles in the big data analytics landscape, with R focusing on statistical analysis and visualization, while Cloudera provides the infrastructure and tools for processing and analyzing large volumes of data at scale. read less

IoT (Internet of Things): IoT refers to the network of interconnected physical devices, vehicles, appliances, and other items embedded with sensors, software, and connectivity, enabling them to collect and exchange data. IoT devices can communicate with each other and with centralized systems to gather and analyze data, leading to improved efficiency, automation, and insights in various domains such as healthcare, agriculture, transportation, and smart homes. Big Data: Big Data refers to large and complex datasets that are difficult to process using traditional data processing applications. Big Data is characterized by its volume, velocity, and variety, meaning it involves large amounts of data generated at high speeds from diverse sources such as sensors, social media, and transactional systems. Big Data technologies and analytics techniques are used to extract valuable insights, patterns, and trends from these datasets, helping organizations make informed decisions and gain a competitive edge. Hadoop: Hadoop is an open-source framework designed for distributed storage and processing of Big Data across clusters of computers. It consists of the Hadoop Distributed File System (HDFS) for storing large datasets across multiple machines and the MapReduce programming model for processing and analyzing these datasets in parallel. Hadoop enables scalable, fault-tolerant, and cost-effective processing of Big Data by distributing tasks across multiple nodes in a cluster. Additionally, Hadoop ecosystem projects such as Hive, Pig, Spark, and HBase provide additional functionalities for data querying, processing, and analysis on top of the Hadoop framework. read less

BigData And Hadoop TestingThe tester needs to know basic programming like Mapper, Hive, Pig Scripts, etc. to verify the scripts and to execute the commands. So, before testing, the testers need to know what all programs and scripts are working, how to write the code and then think about how to test them.

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Working with Big Data presents several challenges, including: 1. **Volume**: Managing and processing massive volumes of data can strain infrastructure and require specialized tools and technologies capable of handling the scale. 2. **Variety**: Big Data comes in various formats, including structured, semi-structured, and unstructured data from different sources such as social media, sensors, and logs. Integrating and analyzing heterogeneous data types can be complex. 3. **Velocity**: Data is generated at high speeds, requiring real-time or near-real-time processing to extract timely insights and respond to events as they occur. 4. **Veracity**: Ensuring the accuracy, reliability, and quality of Big Data can be challenging, especially when dealing with noisy, incomplete, or inconsistent data sources. 5. **Value**: Extracting meaningful insights and actionable intelligence from Big Data requires advanced analytics techniques, domain expertise, and effective data visualization to interpret and communicate findings. 6. **Security and Privacy**: Protecting sensitive data from unauthorized access, ensuring compliance with regulations, and preserving user privacy are critical concerns when working with Big Data. 7. **Scalability**: As data volumes and processing requirements grow, scalability becomes essential to maintain performance and meet evolving business needs. 8. **Infrastructure Complexity**: Deploying and managing distributed computing environments, storage systems, and data processing frameworks can be complex and require specialized skills. 9. **Cost**: Building and maintaining Big Data infrastructure and employing skilled personnel can be costly, requiring organizations to carefully consider the return on investment. 10. **Skills Gap**: Finding and retaining talent with expertise in Big Data technologies, data science, and analytics can be challenging due to the high demand and rapidly evolving landscape. Addressing these challenges requires a combination of technology, processes, skills, and organizational support to harness the full potential of Big Data while mitigating risks and maximizing value. read less

Big Data, Hadoop, Cassandra, and Hive are all related to handling and processing large volumes of data, but they serve different purposes and have distinct characteristics: 1. **Big Data**: Big Data refers to the vast volume, variety, and velocity of data that organizations collect and analyze to gain insights, make informed decisions, and improve operations. It encompasses the entire ecosystem of tools, technologies, and techniques used to manage, store, process, and analyze large datasets. 2. **Hadoop**: Hadoop is an open-source framework for distributed storage and processing of Big Data. It consists of two main components: the Hadoop Distributed File System (HDFS) for storing data across multiple machines, and MapReduce for processing and analyzing data in parallel. Hadoop is designed to handle large-scale batch processing tasks and is particularly well-suited for processing unstructured or semi-structured data. 3. **Cassandra**: Cassandra is a distributed NoSQL database designed for handling large volumes of data across multiple nodes while providing high availability and scalability. It is optimized for write-heavy workloads and offers linear scalability by distributing data across a cluster of commodity hardware. Cassandra is well-suited for real-time, high-throughput applications that require low-latency access to data. 4. **Hive**: Hive is a data warehouse infrastructure built on top of Hadoop that provides a SQL-like query language called HiveQL for querying and analyzing data stored in Hadoop's HDFS. Hive enables users to perform ad-hoc queries, data summarization, and analysis using familiar SQL syntax, making it easier for non-programmers to work with Big Data. Under the hood, Hive translates HiveQL queries into MapReduce jobs or, more recently, Apache Spark jobs for execution on the Hadoop cluster. In summary: - Big Data is a concept encompassing the handling and processing of large volumes of data. - Hadoop is a distributed storage and processing framework for Big Data, consisting of HDFS and MapReduce. - Cassandra is a distributed NoSQL database optimized for high availability and scalability. - Hive is a data warehouse infrastructure built on Hadoop, providing a SQL-like interface for querying and analyzing data stored in HDFS. read less