The volume of data that organizations need to manage is very heterogeneous. Both in public institutions and large organizations, there are numerous types of data. For this, one needs faster, reliable, flexible, and scalable storage and analytics solutions for big data management. The data lakes provide a complete solution to this challenge. This article talks about the basics of Data Lake and its implementation in AWS.
A Data Lake consists of a centralized data repository, which allows storing both structured and unstructured data. It is a location where we can store and manage all types of files, regardless of their sources, scale/format. This data is further used to analyze and achieve the objectives of the organization. Data Lake is used for Big Data Analytics projects in different sectors like public health, R&D, and other business areas. Furthermore, Data Lakes are beneficial for market segmentation in marketing, sales, and Human Resource Department, Data Lakes. Data Lake is of great importance as a data architecture approach. Companies need to manage an increasing variety of information to implement the analysis. This analysis helps them to improve decision-making or better understand their market.
The difference between data lake and data warehouse is the collection of data. In Data Lake, the data collection happens in a natural state. Once done, the data utilization occurs according to the needs of the organization. The Data Lake is a more agile, versatile solution and adapted to users with more technical profiles.
AWS technology offers us a set of services that includes both cloud storage space and analysis tools. These services allow us to combine data and manage the operations we want to perform in a secure and scalable way. Analyze the objective and benefits of implementing a Data Lake with AWS are the initial steps one must take. Once the plan is ready, one will start by migrating data to the cloud in the most efficient way and with the highest possible transfer speed. One must keep the size and volume of data in mind when doing this. For data processing, we will work with serverless-based architecture, coordinated by events for ingesting, processing, and loading on-demand using as a service. For example, AWS Lambda or AWS Glue, allowing processing and transforming a large amount of data efficiently, significantly reducing the cost associated with computing infrastructure and improving performance. The server less architecture allows two types of information processing to be combined: in “batch” mode and in-stream mode when the project requires quick responses and update management of various data flows. With the Lambda function, we can process sales transactions by determining the storage plant to carry out the order. Also, allowing the continuity of the workflow of the complementary process.
Amazon S3 for a data lake provides high scalability, excellent costs, and adequate levels of security. Thus, offering a comprehensive solution to carry out different processing models. With the data in S3, we can use the AWS Glue service to create a data catalog, where users can make queries. The process is complicated when monitoring data flows, configuring access control, and defining security policies. Finally, among the Business Analytic service that Amazon offers us, it would be necessary to implement and execute the best analysis solution. A tool like Amazon Kinesis allows streaming data analysis and processing. A tool like Amazon Athena allows performing interactive analysis with SQL queries instantly.