Big data architecture is designed to handle the ingestion, processing, and analysis of large and complex data. Big Data workloads manage large amounts of data, analyze it for business purposes, steer data analytics operations for business intelligence, and orchestrate the big data analytics tools to effectively extract vital business information from extremely large data pools.

Big data solutions include the following types of workloads:

• Batch processing of large data sources at rest.
• Real-time processing of large amounts of data in motion.
• Interactive exploration of big data sets.
• Predictive analytics and machine learning.

Data Sources include:

• Static files like application log files
• Application data stores
• Structured and unstructured data sets
• Real-time data sources like IoT devices.

Distributed data stores are essential components of the solutions. Data stores range in size from gigabytes to petabytes of data in many different formats. Big data applications process these files using long running batch jobs to filter, aggregate and format the data for later consumption by data analytics.

HDFS(Hadoop Distributed File System)

Hadoop Distributed File System (HDFS) is a component of Big data storage layer. The files in HDFS are broken into block-size chunks called data blocks that are replicated within the cluster for storage resiliency.

YARN(Yet Another Resource Negotiator)

YARN manages the resources for the applications. YARN decouples MapReduce’s management and scheduling capabilities. YARN has multiple nodes to resume execution in case of failure of the first node.

MapReduce

MapReduce's algorithm distributes the job and runs it across the cluster. Single tasks are divided into multiple tasks and run on different machines.

Hadoop

Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware.

Spark

Apache Spark is used for executing data engineering, data science, and machine learning on single-node machines or clusters. It utilizes in-memory caching, and optimized query execution for fast analytic queries against data of any size. It provides API’s in Java, Scala, Python and supports multiple workloads in real time analytics, batch processing, interactive queries, and machine learning. Spark addresses the limitations of MapReduce by doing in-memory processing and reusing data across multiple parallel operations. Spark relies on other storage systems like HDFS, Couchbase, Cassandra and others

Hive

Hive is a distributed data warehouse system. Hive is used to process mostly structured data in Hadoop. Hive allows users to read, write, and manage petabytes of data using SQL. Hive can query large datasets leveraging MapReduce.

Pig

Pig is used for the analysis of large amounts of data. It is a procedural data flow language that operates on the client side of the cluster. It can handle semi-structured data as well.

HBase

HBase is a columnar database that runs on top of HDFS. HBase provides a fault tolerant way of storing data sets and is well suited the process large volumes of random read and write data in real time.

Mahout

Mahout is a library of machine learning algorithms implemented on top of Apache Hadoop and using MapReduce. Mahout provides the data science tools to automatically find meaningful patterns in big data sets.

HCatalog

HCatalog allows you to access Hive Metastore tables with Pig, Spark, and Custom MapReduce applications. It exposes REST API and command line Client to create tables and other operations.

Ambari

Apache Ambari is a open source platform that simplifies the provisioning, management, monitoring, security of an Apache Hadoop cluster by providing an easy to use web UI and REST API. It provides a step by step wizard for installing Hadoop servives.

Zookeeper

Zookeeper provides operational services in Hadoop Cluster. Distributed applications use Zookeeper to store metadata, and use as a distributed configuration service and a naming registry for distributed systems.

Oozie

Apache Oozie is a workflow tool. You can build the workflow with dependencies of various jobs that are bound together and submitted to Yarn as one logical entity. Oozie is like a cron and submits the job to Yarn which executes the job.