A data lake, put simply, is a collection of raw data in the form of BLOBs (Binary Large Objects) or files. A data lake is an archive for structured, semi-structured, and unstructured data from a wide variety of sources. Data lakes allow you to store data in its native format without any fixed limit on account size or files. The metaphor of a lake is apt, in that it is a large container that has multiple tributaries (structured, semi-structured, unstructured, machine-to-machine, logs) flowing in real-time.
A data lake is a scalable, secure platform that allows organizations to ingest data coming in from any system at any speed, store any type or volume of data in its entirety, process data in real-time, and analyze data using various analytics-based applications or programming languages. These utilities have allowed data lakes to be widely adopted by organizations worldwide. Mordor Intelligence valued the data lakes market at USD 3.74 billion in 2020 and also forecasts the market value to reach USD 17.60 billion by 2026, at a CAGR of 29.9%. This article shall be a comprehensive guide to understanding the benefits provided by the adoption of data lakes.
Key Benefits of Data Lake
Data lakes are usually built on low-cost hardware, making it an economically viable choice to store terabytes or even larger volumes of data. Data lakes have the added benefits of providing end-to-end services that reduce the time, labor, and cost required to run data pipelines, streaming analytics, and machine learning workloads on any cloud.
A paper “Data Lakes: Purposes, Practices, Patterns, and Platforms” published in 2017 reported that nearly 23% of companies had a data lake in production. Five years later, the number has only significantly grown. Here are the key benefits that data lakes provide and the major reason behind their wide popularity.
Eliminate Data Silos
For the longest time, data in most organizations were being stored in various locations in a wide variety of ways with no centralized access management system. This made it difficult to access the data and perform detailed analysis on it.
Data lakes have managed to revolutionalize this process and in the process eliminate the need for data silos. A centralized data lake negates the need for data silos by consolidating and cataloguing data and also offering users a single place to look for all sources of data. This makes it easier to analyze massive hordes of data and derive conclusions from it.
No Predefined Schemas
With the deployment of data lakes, the need for predefined schemas no longer exists. Data lakes are known to leverage the Hadoop simplicity to store hordes of data on schema-less write and schema-based read modes, which shows its usefulness in the time of data consumption.
The need to not have any predefined schemas can help maximize your organization’s data value and improve security while minimizing data liability. Data lakes do this by empowering your organization with a cloud-based intelligence capability that provides a low-cost scalable and secure storage solution with superior analysis capabilities on various formats of data.
Adaptable for Modern Use-Cases
The age-old data warehouse solutions are often expensive, proprietary and have massive limitations that make them incompatible with most modern use cases. Data lakes were developed to address this issue and to make sure that they were constantly adaptable to the dynamic situations that most companies were looking to tackle.
Most organizations are looking to deploy machine learning algorithms on unstructured data and also carry out advanced analytics on them. Data lakes offer the required scalability for this even on an exabyte scale. Data lakes also have the added advantage of storing data on flat architectures and object storage as compared to data warehouses which are used to store data in files and folders.
Ability to Store Any Format of Data
One of the biggest advantages of data lakes is that they eliminate the need for data modelling during data ingestion. You can store data in data lakes in any format such as RDBMS, NoSQL Databases, File Systems etc. Data can also be uploaded in its existing format such as log, CSV etc without the need for any sort of transformation.
Another layer of the benefit comes in the form of uncontaminated data. Since data is stored in its raw form, it does not get contaminated and this allows the company to derive insights from the same historical data.
iCCM’s data engine can be leveraged alongside a data lake in order to dramatically improve data quality and reliability.
iCCM helps mitigate the risk of poor-quality data by addressing that risk before each transaction is processed. iCCM never creates, uses, or stores any copies of transactional data. Instead, it uses in-memory processing (popularized by SAP HANA) to read and analyze data directly from the source system while that data (not a copy of it) is “passing through” the iCCM data engine. Besides providing faster performance and a vastly simpler and less vulnerable security profile, this architecture provides a “single version of the truth” and inherently eliminates these two common causes of poor data quality:
- Latency (stale data). Data that passes through the iCCM engine is always the latest –and only — version.
- Poor version control. In systems that rely on duplication of datasets, updates (whether automated or manual, authorized or unauthorized) are sometimes made to some copies and not others, leading to unreliability and a lack of clarity on which copies (if any) are still correct.
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