Overview Of Data Lake
A data lake is a repository in which we can store a large amount of semi-structured, structured and unstructured data. A unique ID with a set of extended metadata tags is assigned to all data elements of a data lake. When a business question arises, you can ask for the relevant data and then analyze smaller data to help answer the question. The lake has a flat architecture, as opposed to a hierarchical data warehouse where data is stored in files and folders. Without first structuring data, you can store your information as it is and we can run various types of analysis such as dashboards and visualizations to a large data processing, real-time analytics, and machine learning to inform better decisions.
A lake is used by professionals such as Data scientists, Data developers, and Business analysts to store a large amount of data.
It used in a lake is un-relational and relational from IoT devices, web sites, mobile applications, etc. In the Schema, it is written in the time of analysis i.e schema on reading. The result after query execution is faster.
Why we Need a Data Lake?
By building a lake, data scientists can see the unrefined view of data.
Reasons for using it are as follows:
The corporation that produces business benefits from their data successfully exceed their peers. In an Aberdeen survey, the corporation that set up a Data Lake was 9% above the organic revenue growth performance of similar companies. These leaders were able to perform new types of analytics such as machine learning through new sources such as log files, clickstream data, social media, and Internet connectivity in the lake.
It supports the importing of data that comes in real time. Data is gathered from multiple resources and then moved to the lake in the original format. A lake provides higher scalability of data. Also, you can know what type of data is in the lake by indexing, crawling, cataloging of the data.
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It supports Data Governance which manages the availability, usability, security, and integrity of data.
It can help the Research & Development teams to test their hypothesis, refine assumptions, and assessment of results.
No silo structure is available.
It offers customers a 360-degree view and a robust analysis.
The quality of the analysis also increases with the increase in data volume, data quality, and metadata.
- Storage engines such as Hadoop have made it easy to store disparate information. There is no need to model data with a Lake into a company-wide scheme.
- The quality of analyzes also increases with the increase in data volume, data quality, and metadata.
- It offers business agility
- It is possible to use machine learning and artificial intelligence to make profitable predictions.
Data lake Architecture on Hadoop, AWS, and Azure
A data lake has two components: storage and calculation. Storage and computing can be either located on-site or in the cloud. This results in the design of a data lake architecture in multiple possible combinations.
1. Hadoop
A distributed server Hadoop cluster solves the big data storage concern. MapReduce is the Hadoop programming model used to divide and process information into smaller subsets in the server cluster.
2. AWS
The AWS product range for its data lake solution is comprehensive. Amazon S3 is at the center of the storage function solution. These Data ingestion tools that allow us to transfer massive amounts of data into S3 are Kinesis Stream, Kinesis Firehose, Snowball and Direct Connect.
In addition to Amazon S3, the NoSQL database, Dynamo DB and Elastic Search offer a simplified process of querying. AWS offers a large range of products with a steep initial learning curve. However, the comprehensive features of the solution are widely used in commercial intelligence applications.
3. Azure
Micro-soft offered the data lake. The Azure data lake has an analytics and storage layer is called Azure Store (ADLS) and the two components that the analytical layer has Azure Analytics and HDInsight. The ADLS standard was built in HDFS and are storage capable of unlimited. It can save trillions of files larger than a petabyte in size with a single file. Azure Store makes it possible for data to be stored and secured and scalable in any format.
Benefits
Some important points are shown below
- Provides unlimited data type value
- Adaptable to changes quickly
- Long-term ownership costs are reduced
- Its the main advantage is centralizing various sources of content
- Users from different departments around the world can have flexible data access
- Provides economical scalability and flexibility
Risk
- It could lose relevance and momentum after some time.
- There is a greater risk when designing
- It also increases the cost of storage & products
- Security and access control is the biggest risk. Sometimes data can be placed in a lake without supervision, as some of the data may need to be protected and regulated.
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