What is Data Lake, It’s Benefits and Architecture?
Data is a piece of information, facts, and statistics often in numerical form collected for reference or analysis. Technology is changing very fast, but as we know, first database, then data warehouse, and now Data Lake technology has emerged. Data Lake is one of the fastest emerging technology which has redefined the data extraction, data storage, and data analysis.
In this article, we will cover the data lake concept, its benefit, architecture, adoption. Let us understand the data lake process first before knowing data lake in detail.
To know what Data Lake is, you need to understand data warehouse first. Once the data warehouse concept is clear, then it will be easy to understand the data lake. The data warehouse is a central location where a business or organizations store its data in a structured form. Any enterprises store its data to fulfill its business requirement like inventory systems, researching a new market for its product, user behavior, interests, and more. Let’s understand the concept of a data warehouse with an example of a retail outlet. If a retail outlet must understand which product from their store had more demand in the last six months and who bought and from which part of the USA, their customers belong. All the data related to customers such as payment method, name, item purchased, and location of the customer. When a customer makes the payment at the POS counter, all their details are stored in a different system from the HR system to sales system to inventory system, and it can be store in other systems as well. But, when the retail outlet must analyze the data, then the business has to extract the data from different systems. The business collects the data from all the different sources and passes it on to ETL, which means Extract, Transform, and Load.
ETL is a concept through which we collect the data based on our requirements, and then we store it in the data warehouse. When all the data is extracted from Data Warehouse, it can be used for data reporting and data analysis.
All the data in a Data Warehouse is store in a structured form, but the technology is improving fast over a while, and the data volume is increasing huge. Now, almost every system like sensors, biometric device is giving data. But remember all these data are not in a structured form. The data are coming into structured, semi-structured, or unstructured form. All this information is useful for businesses. To analyze these data is a very challenging task as the Data Warehouse reads only structured data only. The Data Warehouse works on the concept of Think First Load Later. It means the analyst has to decide what data they need to analyze then accordingly load the data in a structured form. But data are now available in a different form from structured to unstructured to semi-structured. There the role of data lake comes into existence.
The data lake concept is exactly opposite to the data warehouse. The data lake says Load First Think Later. In Data Lake, the data is loaded first and then the businesses decide how to use the data.
What is an example of a data lake?
Let's understand the concept of Data Lake technology with a lake. In a lake, the water comes through a different channel, it comes from streams, rain, river, canal, and store in the raw form. Later the lake water is using for drinking, irrigation, and industries use for their processing purposes.
Data Lake works on the same concept. Data are available in the form of structured, semi-structured or unstructured, images, videos, pdf, or any other form in the Data Lake. So, it is like a central reservoir where data is stored in the raw form. Once the data is stored in Data Lake, then it is used for multiple purposes, it depends on its business purpose. The Data Lake is built along with this concept only where businesses can use all kinds of data according to their needs. A multiple subset of data warehouse is created with structured data to analyse the data.
What is Data Lake?
Data Lake is a large storage repository to hold data in its native format. The data is stored in structured, semi-structured, and unstructured form. The data could be in numerical, image, pdf, video, and other form. An Enterprise Data Lake (EDL) is a data lake for enterprise-wide information storage repository.
Is Data Lake a Database?
A data lake is different from a data warehouse and database. Database and data warehouse stores only the data in a structured form. A data lake, on the other hand, stores all types of data - structured, semi-structured, or unstructured.
Why is Data Lake Required?
Data Lake allows you to store relational data like operational databases and data from line of business applications, and non-relational data like mobile apps, IoT devices, and social media. They also give you the ability to understand what data is in the lake through crawling, cataloguing, and indexing of data.
What are Benefits of Data Lake?
Using Data Lake, the data is managed centrally irrespective of its sources. Once these data are stored these can be combined and processed using Big Data and analytics techniques. Since, the enterprise information is sensitive a proper security mechanism is implemented in Data Lake.
The security measures inside Data Lake provides specific grants to access specific information otherwise the user does not have the access to the original source content.
Once the content is in the data lake, it can be normalized and enriched. This includes metadata extraction, conversion of format, data augmentation, entity extraction, cross linking, aggregation, de-normalization or indexing.
Data is prepared “as needed” reducing preparation costs over up-front processing. A big data compute fabric makes it possible to scale this processing to include the largest possible enterprise-wide data sets.
What is the Difference Between a Data Warehouse and a Data Lake?
Data Processing:
Data Lake and Data Warehouse are two different strategies for storing data. A data warehouse accepts data in structured form only, whereas a data lake accepts data in structured, semi-structured, and unstructured states. As the data lake stores data in all shapes, it is simple to create multiple data warehouses from the data lake.
Source of Data:
Data warehouse stores data in structured from specific sources only. On the other hand, the data lake stores in structured, semi-structured, and unstructured states from any sources in any form.
Data Storage:
Data warehouse is comparatively the most expensive to the data lake for large data volumes. In the data warehouse, queries are higher reliable, faster, and higher performance. But query results are improving for the data lake.
Agility:
Data lake is highly agile, and it can be configured or reconfigured. Data warehouses are less so.
Security:
Data warehouses are generally more secure than data lakes as data warehouse concepts have existed for a longer period and mature now. Whereas data lake security method has the opportunity to mature.
Data Usage:
Data warehouse uses for operational reporting and suits for business users. Whereas Data lake is using for advanced analytics and suit data scientists.
Searching the Data Lake
Data in Data Lake have unstructured and widely varying. The volume of data in Data Lake are very huge. In this environment, search is a necessary tool:
- To find tables that you need - based on table schema and table content
- To extract sub-sets of records for further processing
- To work with unstructured (or unknown-structured) data sets
- And most importantly, to handle analytics at scale
Only search engines can perform real-time analytics at billion-record scale with reasonable cost.
Search engines are the ideal tool for managing the enterprise data lake because:
- Search engines are easy to use – Everyone knows how to use a search engine.
- Search engines are schema-free – Schemas do not need to be pre-defined. Search engines can handle records with varying schemas in the same index.
- Search engines naturally scale to billions of records.
- Search can sift through wholly unstructured content.
Data Lake Architecture
The Data Lake Adoption
The data lakes are adopted because of the following findings:
- Data lakes are increasingly recognizable as both a viable and compelling component within a data strategy, with small and large companies continuing to adopt.
- Governance and security are still top-of-mind as key challenges and success factors for the data lake.