Big Data Lake that Won’t Let You Drown

Big Data Lake that Won’t Let you Drown

Big Data Lake has definitely won over traditional data warehouses and this Big Data Lake Won’t Let you Drown. The ceaselessly moving, fast-paced technology has already bidden adieu to the traditional forms of data storage. With technology boom and information circulating across the globe at lightning speed, traditional data warehouses have become inefficient to hold and process data at an equivalent speed. Their inability to process a large volume of unstructured data and the unsatisfactory late development of appropriate schemas, made way for a new open distributed computing architecture backed by Hadoop and Apache Spark, named as Big Data Lake.
Check out how Wikipedia defines Big Data Lake

Wikipedia defines Big Data Lake as “a large storage repository and processing engine,” which provides a “massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs.” As a matter of fact, this hub-and-spoke integration, loosely termed as Big Data lake, is a way better than the traditional data repositories. Unlike the traditional ways, Data lake can hold a huge volume of raw data in its essence or in its native format. It is remarkable because of some of its unique features such as it being an integrated system that can store volumes of data while diving in for meaningful bits and bytes, and processing and analysing the extracted information henceforth.

Big Data Lake and Hadoop Framework

Evidently, as an open-source framework, Hadoop makes possible the once thought of as an impossible task of managing such a huge volume of data. It can process variety of voluminous data with an unexceptional velocity – hence the 3 Vs that define Bigdata Hadoop: Volume, Velocity, and Variety.
A data lake, in a way, becomes an effective arena of operational processes that stores and manages a wide variety of data pouring in from diverse sources. Big data lake is generous enough to hold terabytes and petabytes of social media data, clickstream logs, and other log files. It acts as a storehouse for data collected from traditional RDBMSes, and unlike traditional data warehouses, data lake does not impose any restrictions regarding schema and security.
Another noticeable feature that makes Hadoop exceptionally amazing in the data lake development is the provision of horizontal scaling against the vertical scaling. Where Vertical Scaling always involved adding a CPU or RAM to the existing Server, providing no actual or permanent solutions, Horizontal Scaling involves addition of more machines or setting up a cluster; by adding or removing nodes in no time.

How Hadoop Processes Data?

In addition, Hadoop ecosystem tools act as powerful resources that enable efficient data streaming and stream processing which involves effective ingestion, storage, and recovery of the concerned or the company-specific data.
The hadoop ecosystem tools transform Big data lake into enterprise-ready solution by carrying out such processes and by making data industry-ready for further analysis. Central to Hadoop ecosystem is HDFS, a distributed file storage system with MapReduce as its processing engine. HDFS is chiefly instrumental in storing voluminous data sets. TechTarget, a bigdata analytics provider, claims that HDFS “provides high-performance access to data across Hadoop clusters.”
The hadoop ecosystem or the techno-stack employs various tools such as Kafka, Flume, Sqoop, so on and so forth that help in data streaming and processing. Hadoop ecosystem tool Kafka, with its stream based architecture, becomes one of the central repositories of data streams. On the other side, Flume, with its simple and flexible architecture, enacts the hadoop ingestion tool, which ensures pushing in the data, or moving voluminous streaming data into Hadoop Distributed File System (HDFS).
Data can also be transferred by using another hadoop ecosystem tool Sqoop, which transfers data between Hadoop and other relational databases, including databases such as MySQL and Oracle, making it ready for analytics. Oozie is yet another tool that pipelines all kinds of programs to synchronize the data flow with Hadoop’s distributed environment. No doubt, data lake development depends on the requirements of different commercial houses, but it has definitely emerged as a reliable repository of large bigdata, while simultaneously processing data for the most effective data analysis.

Hadoop Technostack is therefore reigning supreme with its exceptionally well hadoop ecosystem tools working wonders in processing huge amount of bigdata. With its steep rising graph, there is an ever-rising demand of hadoop professionals and this becomes the primary reason why ETLhive is providing the best training in Hadoop in which the concepts such as Pig, Hive, Sqoop, MapReduce, Flume, Kafka, Oozie, MongoDB, Elastic Search, and Spark and Scala are discussed thoroughly, followed by the deployment of projects on Amazon Web Services. With industry-oriented training at ETLhive, and with the knowledge of Hadoop Technostack and Data Lake, you will certainly be “sought-after” for high profiles in the IT industry, and Data Lake, in its own superiority, will never let you down or drown!

 

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