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I am familiar with the concept of "Big Data" but how does "Data Lake" differ from Big Data? Is it derived from Big Data? Please explain.

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Big Data is a term related to extraction of information from big datasets. It is also sometimes used in context of predictions from large datasets. Big Data points to the 'big' aspect of data.

Data lake is a concept of storing and providing data in data system, no matters what size of data it is. Data lake aims to be a single repository of data in a company for better management and access to the data.

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What is a data lake?

A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data, and run different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions.

Why do you need a data lake?

Organizations that successfully generate business value from their data, will outperform their peers. An Aberdeen survey saw organizations who implemented a Data Lake outperforming similar companies by 9% in organic revenue growth. These leaders were able to do new types of analytics like machine learning over new sources like log files, data from click-streams, social media, and internet connected devices stored in the data lake. This helped them to identify, and act upon opportunities for business growth faster by attracting and retaining customers, boosting productivity, proactively maintaining devices, and making informed decisions. Data Lakes compared to Data Warehouses – two different approaches

Depending on the requirements, a typical organization will require both a data warehouse and a data lake as they serve different needs, and use cases.

A data warehouse is a database optimized to analyze relational data coming from transactional systems and line of business applications. The data structure, and schema are defined in advance to optimize for fast SQL queries, where the results are typically used for operational reporting and analysis. Data is cleaned, enriched, and transformed so it can act as the “single source of truth” that users can trust.

A data lake is different, because it stores relational data from line of business applications, and non-relational data from mobile apps, IoT devices, and social media. The structure of the data or schema is not defined when data is captured. This means you can store all of your data without careful design or the need to know what questions you might need answers for in the future. Different types of analytics on your data like SQL queries, big data analytics, full text search, real-time analytics, and machine learning can be used to uncover insights.

As organizations with data warehouses see the benefits of data lakes, they are evolving their warehouse to include data lakes, and enable diverse query capabilities, data science use-cases, and advanced capabilities for discovering new information models. Gartner names this evolution the “Data Management Solution for Analytics” or “DMSA.”

What is a data lake? | aws

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Data Lakes:

What: The data lake is a general purpose, highly scalable, low-cost storage tier for raw data. If the data warehouse is a source of purposeful structured data that has been preprocessed and loaded to answer specific business-centric questions, the data lake is the staging area for data that might have importance in the future.

Why?

Given that businesses are moving to a “store everything” mentality across most of the data they produce, it is essential to have a storage tier that can scale to meet the storage needs of tomorrow while also providing benefits today.

Challenges:

The challenges of storing raw data, however, can manifest when the time comes to make use of and provide purpose to these deep pools of raw potential. The challenge is that over the months and years that data is sitting and waiting, changes to the data being produced and a lack of records as to what exactly was being recorded at various points can make older data unusable. This can dramatically increase the lead time to make use of the data or cause years of data to be thrown away due to corruption of data over time.

Technical tip:

So even though the data lake is conceptually a storage area for raw, under-purposed data, there is still a need to provide a moderate level of effort in order to ensure that data corruption doesn’t occur along the way, and that the business can make practical use of the data when the time comes to put it into action.

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