Many people still wonder what is Apache Hadoop. It has to do with big data, Hortonworks is involved but what does it really consist of?? Apache Hadoop is an open source framework that enables distributed storage and processing of large data sets based on commercial hardware. In other words, Hadoop enables institutions to quickly obtain information from massive amounts of structured and unstructured data, positioning them at the level of current market demands in terms of dynamism and capacity.
The Hadoop ecosystem has solutions of all kinds to cover any need that the business may have regarding:
It is exactly these functionalities that best define what is Apache Hadoop although, to truly know the possibilities of this tool and the secret of its versatility, it is necessary to understand the origin of the benefits it brings; those that drive many corporations to opt for this alternative for their big data projects. All the benefits of Hadoop focus on some of its main qualities:
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Scalability: this tool enables you to store and distribute huge data sets on its hundreds of servers that operate in parallel, allowing you to forget the limits imposed by other alternatives.
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Speed– Ensures processing efficiency that no one can match, How else can terabytes of information be processed in minutes?
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Cost effectiveness: Data storage becomes a reality for companies as the investment required goes from tens of hundreds of euros per terabyte to hundreds of euros per terabyte.
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Flexibility: New data sources? No problem, New types of data? of course… Apache Hadoop adapts to the needs of the business and accompanies it in its expansion, providing real solutions for any initiative that arises.
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Resistance to failure: its tolerance to errors is one of its attributes best valued by users since all the information contained in each node is replicated in other nodes of the cluster. In case of failure, there will always be a copy ready to be used.
What is Apache Hadoop: enterprise Solutions
Every problem needs a solution and, therefore, get closer to discover what is Apache Hadoop implies entering the Apache Software Foundation projects. Each of them has been developed to offer an explicit function and, therefore, each has its own community of developers, as well as individual release cycles. Tools, integrate and work with Hadoop it's related to:
1. Data management: The goal is to store and process large amounts of data in a scalable storage layer and, to make it, Hadoop Distributed File System is coming (HDFS). This technology, that works by means of inexpensive hardware, lays the foundation for efficient scale from the storage tier. It is also based on Apache Hadoop THREAD, Provides pluggable architecture and resource management to enable a wide variety of data access methods, which makes it feasible to operate with data stored in Hadoop at the desired performance and service levels. In short Apache Tez, what does magic do, processing big data in near real time, thanks to its generalization of the paradigm Small map that gains in efficiency.
2. Access to data: You cannot have the perspective necessary to answer the question of what Apache Hadoop is without knowing that one of its strengths is the accessibility that it guarantees, by allowing you to interact with data in a wide variety of ways and in real time. The applications that achieve this are:
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Apache Hive: the most widely adopted data access technology.
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Small map: enabling you to build applications that process large amounts of structured and unstructured data in parallel.
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Apache pig: a platform for the processing and analysis of large data sets.
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Apache HCatalog: which provides a centralized way for data processing systems that makes it possible to understand the structure and location of data stored in Apache Hadoop.
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Apache Hive: data warehouse that enables easy summarization and ad-hoc query launch via SQL-equivalent interface for large data sets stored in HDFS.
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Apache HBase: NoSQL column-oriented data storage system that provides access to read or write big data in real time for any application.
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Apache storm: adds reliable real-time data processing capabilities.
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Apache Kafka: is a fast and scalable publish-subscribe messaging system that is often used in place of traditional message brokers due to its high performance, replication and fault tolerance.
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Apache mahout– Provides scalable machine learning algorithms for Hadoop that greatly assist data scientists in their clustering tasks, sorting and filtering.
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Apache Accumulation– A high-performance data storage device that includes recovery systems.
3. Governance and data integration: enables fast and efficient data loading based on the intervention of:
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Apache Falcon: is a data management framework that simplifies data lifecycle management and processing, which enables users to configure, manage and orchestrate data movement, parallel processing, error recovery and data retention; policy-based governance.
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Canal Apache– It enables you to move, in an aggregate and efficient way, large amounts of log data from many different sources to Hadoop.
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Apache Sqoop– Streamlines and facilitates the movement of data in and out of Hadoop.
4. Security: Apache Knox is responsible for providing a single point of authentication and access to the Apache Hadoop services in a group. Thus, simplicity in terms of security is ensured, both for users accessing cluster data, as for the operators who are in charge of managing the cluster and controlling its access.
5. Operations: Apache Ambari provides the essential interface and APIs for provisioning, Hadoop cluster management and monitoring and integration with other management console software. Apache Zookeeper coordinates distributed processes, enabling distributed applications to store and mediate changes to important configuration information. Finally, Apache Oozie is in charge of guaranteeing the work logic in the programming tasks.
Today, with the new serverless platforms, Cloud, Spark, Kafka and the rise of data engineering, Apache Hadoop has lost some relevance. It is the logical consequence of the transition from business intelligence and big data to artificial intelligence and machine learning.. Despite this, despite the changes, this technology and its ecosystem will continue to adapt to, presumably, lead again, sometime, digital evolution, as they already did in their day.
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