How a data lake can store enormous amounts of data
Data growth is exploding! With the increasing number of mobile devices, intelligent sensors and smart endpoints generating an ever increasing variety, volume and velocity of data.
This next generation of cloud storage is ideal for building vast storage repositories where you can collect massive volumes of raw data.
What is a Data Lake?
It is a massive pool of storage that can contain any type of data, structured, semi structured or unstructured.
It uses object storage to store all data types, each piece of data has a metadata tag and a unique identifier, this makes it easy to identify blocks of data containing the word i.e. cinema, budget, release date from this the software application can then retrieve all this information from the cloud repository and present it in a viewable format or import the information into a database. By leveraging inexpensive object storage and open formats, a lake enable many applications to easily discover information that would otherwise be contained in Petabytes of storage and difficult, if not impossible to find.
This breaks down the traditional corporate information silos by bringing all the enterprise’s data into a single repository for analysis, without the historical restrictions of data transformation.
They provide the foundation for advanced analytics, machine learning and new data driven business practices. Data scientists, business analysts and technical professionals can run analytics in place using the commercial or open-source data analysis visualisation and business intelligence tools of their choice. Various vendors offer standards-based tools, from self-service data exploration tools for non-technical business users to advanced data mining platforms for data scientists that help enterprises monetise data lake investments and transform raw data into business value.
For example, a data repository for Internet of Things (IoT) implementation may use Edge computing devices to process and analyse local data before sending it to the data lake. For example, edge servers might perform real time analytics, execute local business logic and filter out data that has no intrinsic historical or global value.
You can implement a data lake in an enterprise data centre or in the cloud. Many early adopters deployed data repositories on premise. As data repositories become more prevalent, many mainstream adopters are looking to cloud based data lakes to accelerate time to value reduce TCO and improve business agility.
They are often used to consolidate all of an organisation’s data in a single, central location, where it can be saved “as is,” without the need to impose a schema (i.e. a formal structure for how the data is organised). Data in all stages of the refinement process can be stored in a data lake: raw data can be ingested and stored right alongside an organisation’s structured, tabular data sources (like database tables), as well as intermediate data tables generated in the process of refining raw data.
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