Hey,
It’s Sarvar Nadaf again, a senior developer at Luxoft. I worked on several technologies like Cloud Ops (Azure and AWS), Data Ops, Serverless Analytics, and Dev Ops for various clients across the globe.
What is a Data Lake?
A "data lake" is a centralized repository where we can store all your structured and unstructured data at any scale. It can process any type of data, regardless of its variety or magnitude, and save it in its original format. You can run several sorts of analytics, from dashboards and visualizations to big data processing, real-time analytics, and machine learning to help you make better decisions, without first structuring your data.
Businesses can store any type or volume of data in full fidelity using a data lake, process data in real-time or batch mode, and analyze data using SQL, Python, R, or any other language, third-party data, or analytics applications. A data lake offers businesses a scalable and secure platform that enables them to ingest any type of data (structured, semi-structured, and unstructured) from any location, whether it originates from bare metal, the cloud, hybrid cloud, or any other computing system.
Data Lake vs Data Warehouse:
A data warehouse is a database designed for the analysis of relational data from corporate applications and transactional systems. In order to optimize for quick SQL queries, the data structure and schema are set in advance. The results are often utilized for operational reporting and analysis. A data warehouse prefers structured data, and the schema must be available for reading the data.
As implied by its name, a data lake stores all three forms of data structured, semi-structured, and unstructured from a variety of sources, including sensors, CCTV, the internet of things, and more. We don’t need to worry about the data type or schema because we have already read that data warehouses require schema on write and deal only with structured data. In the data lake, we only need to store the data, and we will determine what to do with it later.
While we typically execute batch reporting, business intelligence, and visualizations in data warehousing, we also perform operations like machine learning, predictive analytics, data discovery, deep learning, and profiling in data lakes.
The main and major difference between data warehousing and data lake is in data warehousing, we need to think before we load data. In the data lake, we load the data and decide later what to do with it. Data warehousing required schema on a write whereas data lake required scheme on read.
Data Lake on AWS:
AWS managed services that assist with data ingest, storage, retrieval, processing, and analysis are among them. AWS provides Data Lake on AWS, which deploys a highly available, cost-effective data lake architecture on the AWS Cloud together with a user-friendly UI for searching and requesting datasets, to help our clients as they develop data lakes.
To obtain insights from your unstructured data sets, you may leverage native AWS services to run big data analytics, artificial intelligence, machine learning, high-performance computing, and deep learning using data lakes built on Amazon S3. End-to-end data integration and centralized, database-like rights and control are made simple when used in conjunction with AWS Lake Formation and AWS Glue. Using AWS Kinesis and AWS MKS, we may ingest data into the AWS S3 based data lake. We can easily perform direct queries on all the data present in the data lake using AWS analytical products like AWS Lambda, AWS Glue, Amazon EMR, and Amazon Athena.
Advantages of AWS Data Lake:
1. Cost-efficiency:
With its pay-as-you-go strategy, AWS makes it easier for businesses to store and manage large amounts of data in its Data Lake. Data lakes are substantially less expensive to operate than data warehouses since they don’t require structured data.
2. Flexibility:
Businesses can store enormous amounts of data in their original format in AWS data lakes without organizing or describing it first. This gives analysts more flexibility when studying things like syndicated, POS, and big data, when a warehouse has trouble maintaining structural consistency across diverse sources. Users can obtain all information much more quickly and simply with an AWS data lake.
3. Scalability:
The S3 service, which is used by AWS Data Lake, enables us to store enormous amounts of data from any source at any size. Any type of data can be stored in the AWS Data Lake without running out of resources.
4. Secure:
AWS Data Lake offers a variety of security options that enable businesses to store data without any problems. AWS Lake Formation is a service that manages data collection, filtration, transmission, and cataloging while also making it securely available.
AWS Lake Formation:
The AWS Lake Formation overview that we are discussing here only as the AWS Data Lake is entirely governed by the AWS Lake Formation, so you should know about the AWS Lake Formation.
Building, securing, and managing data lakes is simple with the help of AWS Lake Formation, a fully managed service. Many of the difficult manual tasks that are typically necessary to construct data lakes are simplified and automated by lake formation. These actions entail gathering, purifying, transferring, and cataloging data, as well as securely making it accessible for analytics and machine learning.
We will be able to handle the petabytes of data on the AWS Data Lake with the help of the AWS Lake Formation without having to worry about data storage, data security, or data governance. We can create a data lake using the AWS S3 service’s simple storage solution thanks to lake formation. We can handle all the data in one place and secure the data lake using the AWS lake formation service. AWS offers the data lake the finest security and control. In the meantime, feel free to ask me anything in the comment box below. We will cover AWS Data Lake in more detail in forthcoming blogs.
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Here is the End!
I hope you like my article. I’m going to share my knowledge with you in order to make it easier for you to grasp AWS Data Lake. I’ll be publishing more articles like this soon.
happy studying!
