Quote: "Apache Hadoop is a collection of open-source software utilities that facilitates using a network of many computers to solve problems involving massive amounts of data and computation."
Frameworks and tools used for building distributed systems, including Hadoop, Spark and others.
Network protocols: Understanding different networking protocols such as TCP/IP, HTTP and UDP is important when dealing with distributed computing.
Distributed architecture: Knowing the different types of architectures such as client-server, peer-to-peer, and hybrid is important when building distributed systems.
High availability and fault tolerance: Understanding redundancy, replication, and failure recovery mechanisms is essential to ensuring high availability and fault tolerance in a distributed system.
Cloud computing: Knowing how to provision, deploy, and manage resources on cloud platforms like Amazon Web Services (AWS) or Microsoft Azure is essential for building distributed systems.
Communication patterns: Understanding different communication patterns such as publish-subscribe, message queuing, and remote procedure calls is important when building distributed systems.
Data consistency: Ensuring data consistency in a distributed system can be challenging, so knowing different consistency models such as eventual consistency, strong consistency, and causal consistency is important.
Scalability: Building scalable systems that can handle increased workloads is a major concern in distributed systems. Understanding different scaling techniques such as horizontal and vertical scaling is important.
Cluster management: Knowing how to manage a cluster of machines, including resource allocation and task scheduling, is important in distributed systems.
Containerization: Containerization technologies such as Docker and Kubernetes can be used to deploy and manage distributed systems.
Big data processing: Distributed computing is often used for big data processing, so knowing technologies like Apache Hadoop, Spark, and Flink is important.
Microservices: Microservices architecture is becoming increasingly popular in distributed systems, so knowing how to design and implement microservices is important.
Security: Security is an important consideration in any distributed system, so knowing about authentication, authorization, and encryption is important.
Performance monitoring: Knowing how to monitor the performance of distributed systems, including metrics like latency and throughput, helps ensure that they are operating efficiently.
Virtualization: Virtualization technologies like hypervisors can be used to deploy and manage virtual machines in distributed systems.
Data storage: Storing data in a distributed system can be challenging, so knowing different data storage technologies such as relational databases, NoSQL databases, and distributed file systems is important.
Apache Hadoop: It is a widely used open-source distributed computing framework that is used to process large amounts of data in parallel across clusters of commodity hardware.
Apache Spark: It is an open-source distributed computing framework that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.
Apache Storm: It is a distributed real-time computation system that is used for processing vast amounts of data in real-time.
Apache Kafka: It is a distributed streaming platform that is used for building real-time data pipelines and streaming applications.
Apache Flink: It is a distributed stream processing framework that provides fast, reliable, and accurate data processing.
Mesos: Apache Mesos is a distributed systems kernel that is used for managing and executing applications on large-scale clusters.
Kubernetes: It is an open-source container orchestration platform that is used for running and managing containerized applications in a distributed environment.
Zookeeper: It is a distributed coordination service that is used for maintaining configuration information, naming, providing distributed synchronization, and providing group services.
Docker Swarm: It is a container orchestration platform that enables the deployment and management of containerized applications at scale.
Akka: It is a distributed computing framework that helps in building reactive, fault-tolerant, and event-driven applications.
OpenMPI: It is a message passing interface that is used for building scalable parallel applications on a distributed system.
TensorFlow: It is an open-source machine learning framework that is used for building and deploying machine learning models in a distributed environment.
Microsoft Azure Batch: It is a cloud-based distributed computing platform that is used for running parallel compute applications across many nodes in the cloud.
Google Cloud Dataflow: It is a fully managed service for executing batch and stream data processing pipelines.
Apache Beam: It is an open-source unified programming model for batch and stream processing, allowing developers to build complex data processing pipelines.
Redis: It is an in-memory data store that supports distributed execution of functions or stored procedures.
Ray: It is an open-source platform for building distributed applications, including reinforcement learning, hyperparameter tuning, and other compute-intensive tasks.
PyTorch: It is an open-source machine learning framework that supports distributed training of deep neural networks.
Apache Geode: It is a distributed in-memory data grid that is used for scaling out distributed applications.
Hazelcast: It is an in-memory data grid that provides distributed storage and computation for Java applications.
Quote: "It provides a software framework for distributed storage and processing of big data using the MapReduce programming model."
Quote: "Hadoop was originally designed for computer clusters built from commodity hardware, which is still the common use."
Quote: "All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common occurrences and should be automatically handled by the framework."
Quote: "The core of Apache Hadoop consists of a storage part, known as Hadoop Distributed File System (HDFS), and a processing part which is a MapReduce programming model."
Quote: "Hadoop splits files into large blocks and distributes them across nodes in a cluster."
Quote: "This approach takes advantage of data locality, where nodes manipulate the data they have access to. This allows the dataset to be processed faster and more efficiently."
Quote: "The base Apache Hadoop framework is composed of the following modules: Hadoop Common, Hadoop Distributed File System (HDFS), Hadoop YARN, and Hadoop MapReduce."
Quote: "Hadoop YARN is a platform responsible for managing computing resources in clusters and using them for scheduling users' applications."
Quote: "Hadoop Ozone is an object store for Hadoop."
Quote: "The term Hadoop is often used for both base modules and sub-modules and also the ecosystem, or collection of additional software packages that can be installed on top of or alongside Hadoop."
Quote: "Apache Hadoop's MapReduce and HDFS components were inspired by Google papers on MapReduce and Google File System."
Quote: "The Hadoop framework itself is mostly written in the Java programming language, with some native code in C and command line utilities written as shell scripts."
Quote: "Though MapReduce Java code is common, any programming language can be used with Hadoop Streaming to implement the map and reduce parts of the user's program."
Quote: "Apache Pig, Apache Hive, Apache HBase, Apache Phoenix, Apache Spark, Apache ZooKeeper, Apache Impala, Apache Flume, Apache Sqoop, Apache Oozie, and Apache Storm."
Quote: "Hadoop provides a software framework for distributed storage and processing of big data using the MapReduce programming model."
Quote: "HDFS is a distributed file-system that stores data on commodity machines, providing very high aggregate bandwidth across the cluster."
Quote: "Hadoop MapReduce is an implementation of the MapReduce programming model for large-scale data processing."
Quote: "Hadoop takes advantage of data locality, where nodes manipulate the data they have access to, allowing the dataset to be processed faster and more efficiently."
Quote: "All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common occurrences and should be automatically handled by the framework."