A data lake is a method of storing data within a system or repository, in its natural format, that facilitates the collocation of data in various schemata and structural forms, usually object blobs or files. The idea of data lake is to have a single store of all data in the enterprise ranging from raw data (which implies exact copy of source system data) to transformed data which is used for various tasks including reporting, visualization, analytics and machine learning. The data lake includes structured data from relational databases (rows and columns), semi-structured data (CSV, logs, XML, JSON), unstructured data (emails, documents, PDFs) and even binary data (images, audio, video) thus creating a centralized data store accommodating all forms of data.
A data swamp is a deteriorated data lake, that is inaccessible to its intended users and provides little value.
Video Data lake
Background
James Dixon, then chief technology officer at Pentaho allegedly coined the term to contrast it with data mart, which is a smaller repository of interesting attributes extracted from raw data. He argued that data marts have several inherent problems, and promoted data lakes. These problems are often referred to as information siloing. PricewaterhouseCoopers said that data lakes could "put an end to data silos. In their study on data lakes they noted that enterprises were "starting to extract and place data for analytics into a single, Hadoop-based repository."
Many companies have now entered into this space: Google, Microsoft, Zaloni, Teradata, Cloudera, and Amazon all have data lake offerings to name a few. Some offer cloud-based services, such as Cazena's "Data Lake as a Service" on AWS or Azure.
Maps Data lake
Examples
One example of a data lake is the distributed file system used in Apache Hadoop.
Many companies also use cloud storage services such as Azure Data Lake and Amazon S3. There is a gradual academic interest in the concept of data lakes, for instance, Personal DataLake at Cardiff University to create a new type of data lake which aims at managing big data of individual users by providing a single point of collecting, organizing, and sharing personal data.
Analytics is a primary reason why this new kind of data architecture is becoming popular. There are many advantages when working with a variety of data structures in "big data" analysis, but caution should be taken as there are positives and negatives to using this data architecture.
An earlier data lake (Hadoop 1.0) had limited capabilities with its batch oriented processing (Map Reduce) and was the only processing paradigm associated with it. Interacting with the data lake meant you had to have expertise in Java with map reduce and higher level tools like Pig & Hive (which by themselves were batch oriented). With the dawn of Hadoop 2.0 and separation of duties with Resource Management taken over by YARN (Yet Another Resource Negotiator), new processing paradigms like streaming, interactive, on-line have become available via Hadoop and the Data Lake.
Criticism
In June 2015, David Needle characterized "so-called data lakes" as "one of the more controversial ways to manage big data". PricewaterhouseCoopers were also careful to note in their research that not all data lake initiatives are successful. They quote Sean Martin, CTO of Cambridge Semantics,
They describe companies that build successful data lakes as gradually maturing their lake as they figure out which data and metadata are important to the organization.
One other criticism about the data lake is that the concept is fuzzy and arbitrary. It refers to any tool or data management practice that does not fit into the traditional data warehouse architecture. The data lake has been referred to as a technology such as Hadoop. The data lake has been labeled as a raw data reservoir or a hub for ETL offload. The data lake has been defined as a central hub for self-service analytics. The concept of the data lake has been overloaded with meanings, which puts the usefulness of the term into question.
The data in Data Lakes should not have indefinite life in the repository to make it data swamp. Most of corporate companies who manage data lakes define effective data archival or data removing techniques and procedures to keep the pond within controllable limits.
References
Source of the article : Wikipedia
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