A data lake is a flexible, affordable data repository that can store vast quantities of both organized and unstructured data. It can be used by organizations to save data in its original form, find it, examine it, and then modify it as needed.
In this article, we will be discussing the below-mentioned aspects of AWS Data Lake:
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What is a Data Lake?
A data lake is a centralized location where both organized and unstructured data are stored. It is a site where we may store and manage all types of files, independent of their source, scale, or format, to do analysis, visualization, and processing by the organization’s goals.
To give you an example, Data Lake is utilized for Big Data Analytics projects in a variety of industries, ranging from public health to R&D, as well as in many business domains such as market segmentation, marketing, Sales, and HR, where Business Analytics solutions are critical.
When employing a data lake, all data is retained; none is deleted or filtered before storage. The data could be analyzed immediately, later, or never at all. It could also be reused numerous times for multiple purposes, as opposed to when data has been polished for a certain purpose, making it difficult to reuse data in a new way.
Why build Data Lake on Amazon S3?
AWS S3 is built for data durability of 99.999999999%. With that level of durability, you should only expect to lose one object every 10,000 years if you save 10,000,000 objects in Amazon S3. All uploaded S3 items are automatically copied and stored across many systems by the service. This ensures that your data is always available and safe from failures, faults, and threats.
Other features include:
- Security by design
- Scalability on demand
- Durable
- Integration with 3rd party service providers
- Vast data management features
AWS Data Lake Architecture
A data lake is an architecture pattern, not a specific platform, that is constructed around a large data store that employs a schema-on-read approach. In Amazon data lake, you store vast amounts of unstructured data in object storage, such as Amazon S3, without pre-structuring the data but with the option to do future ETL and ELT on the data.
As a result, it is perfect for enterprises that require the analysis of constantly changing data or very huge datasets.
Even though there are many distinct data lake architectures, Amazon offers a standard architecture with the following components:
- Stores datasets in their original form, regardless of size, on Amazon S3
- Ad hoc modifications and analyses are performed using AWS Glue and Amazon Athena
- In Amazon DynamoDB, user-defined tags are stored to contextualize datasets, enabling governance policies to be implemented and datasets to be accessed based on their metadata.
- A data lake with pre-integrated SAML providers like Okta or Active Directory is created using federated templates.
The architecture is composed of 3 major components:
- Landing zone – In the AWS landing zone, the raw data is ingested from a variety of sources, both inside and outside the company. Data modeling and transformation are absent.
- Curation zone – You perform extract-transform-load (ETL) at this step, crawl data to identify its structure and value, add metadata, and use modeling techniques.
- Production zone – consists of processed data that is ready for usage by business apps, analysts, data scientists directly, or both.
Steps for deploying reference architecture-
- For deployment of infrastructure components, AWS CloudFormation is used
- For creating data packages, ingestion of data, creating manifest, and performing administrative tasks API Gateway and Lambda functions are used.
- The core microservices store, manage, and audit data using Amazon S3, Glue, Athena, DynamoDB, Elasticsearch Service, and CloudWatch.
- With Amazon CloudFront acting as the access point, the CloudFormation template builds a data lake console in an Amazon S3 bucket. It then creates an administrator account and sends you an invitation through email.
Amazon offers many templates for easily deploying this architecture in Amazon accounts.
AWS Data Lake best practices
Let’s discuss some of the best practices that will help you optimize your AWS data lake, and help in reducing costs, decreasing time-to-insights, and getting the most value from your Amazon Data Lake deployment:
Ingestion
Amazon advises keeping data in its original format after ingesting it. Any data transformation should be saved to a different S3 bucket so you can go back and perform fresh analyses on the original data.
Although this is a smart practice, S3 will include a lot of out-of-date information. Using object lifecycle policies, you should specify when this data should be transferred to an archive storage tier, such as Amazon Glacier. By doing this, you may still access the data as needed while saving money.
Organization
Consider organization right from the start of a data lake project:
- Data must be organized in partitions in various S3 buckets
- Keys must be generated for each partition that will help in identifying them with common queries
- Partitioning buckets in day/month/year format is recommended in the absence of any good organizational structure
Preparation
For various forms of data, treatment, and processing should be handled differently:
- Use redshift or Apache HBase for changing data dynamically
- Immutable data can be stored in S3 for performing transformations and analysis
- Use Kinesis to stream data, Apache Flink to process it, and S3 to store the output for quick ingestion.
To allow you to customize your deployment and enable continuous data management, Amazon offers AWS Lake Formation.
The development, security, and management of your data lake are made easier with Lake Formation, a fully managed service. It simplifies the difficult manual activities that are often necessary to create a data lake, including:
- Collecting data
- Moving data to the data lake
- Organizing data
- Cleansing data
- Making sure data is secure
To build a data lake, Lake Formation scans data sources and automatically puts data into Amazon Simple Storage Service (Amazon S3).
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Lake Formation handles the following functions, either directly or indirectly via other AWS services such as AWS Glue, S3, and AWS database services:
- Your data is registered for S3 routes and buckets.
- makes data flows to take in and handle raw data as necessary.
- creates data catalogs with metadata pertaining to your data sources.
- Data access controls are established using a rights/revocation method for both metadata and real data.
Using their favorite analytics tools, such as Amazon Athena, Redshift, or EMR, end users can access and interact with the data after it has been stored in the data lake.
Conclusion
By preserving data in a centralized repository in open standards-based data formats, data lakes help you break down boundaries, use a variety of analytics services to extract the most information from your data, and cost-effectively grow your storage and data processing needs over time.
Most comprehensive big data platforms are accessible through AWS data lake. In addition to providing secure infrastructure, AWS also offers a wide range of scalable, affordable services for gathering, storing, classifying, and analyzing data in order to gain insightful information.
In order to satisfy your specific data analytic requirements, AWS makes it simple to build and modify a data lake. You can get going by utilizing one of the available Quick Starts or by relying on an APN partner to execute one for you by utilizing their expertise and knowledge. Data that is organized and unstructured can both be found in a data lake.