The volume of data that organizations need to manage is very heterogeneous and this happens in both public institutions and big organizations. There are more types of data than ever, fast, flexible and scalable storage and analytics solutions are needed for managing big data.

Data Lakes seems to provide a very good solution to this challenge.

 

In this article, we will explain what a Data Lake is and how it can be implemented thanks to the Amazon Web Services (AWS) cloud platform.
If you want to see a use case, you can find here an example of a Data Lake implementation with AWS designed and implemented by Nubersia.

What is a data lake?

A data lake is a centralized data repository, which allows you to store both structured and unstructured data. It is a location where we can store and manage all types of files, regardless of their source, scale, or format, in order to carry out analysis, visualization, and processing in accordance with the organization’s objectives.

To give you an idea, Data Lake is used, for example, for Big Data Analytics projects in different sectors, from public health to R & D, and also in different business areas such as market segmentation, marketing, and Sales or Human Resources, where Business Analytics solutions are really vital.

All data is kept when using a data lake; none of it is removed or filtered prior to storage. The data might be used for analysis soon, in the future, or never at all. Data could also be used many times for different purposes, as opposed to when the data has been refined for a specific purpose, which makes it difficult to reuse data in a different way.

Data lake vs. data warehouse

The peculiarity of Data Lake compared to other unified repositories such as data warehouses (Data WareHouse) is that data is collected in a natural state and is transformed at the moment to respond to the processing needs of the organization.

The implementation of a data lake saves time in the process of selecting and structuring raw data, as well as the need to understand business processes to create a model adaptable to the users of the organization.

Data Lake is a more agile, versatile solution that is also adapted to users with more technical profiles, with more advanced analysis needs:

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AWS Data Lake: How to implement a Data Lake on AWS

AWS technology offers us a set of services that includes both storage and analysis tools that allow us to combine data and manage the operations we want to perform in a secure and scalable way.

The first step we must take is to analyze the objectives and benefits that we want to achieve with the implementation of a Data Lake with AWS. Once the plan is designed, we will begin by migrating the data to the cloud in the most efficient way and with the highest possible transfer speed, taking into account the size and volume of data.

For data processing, we will work with an architecture based on serverless, coordinated by events for ingestion, processing, and loading on-demand, using AWS Lambda or AWS Glue as a service, for example, allowing to process and transform a large amount of data efficiently, significantly reducing the costs associated with computing infrastructure and improving performance.

The Serverless architecture allows combining two types of information processing: in “batch” mode (treating volumes of data for spaced periods of time and executed on a scheduled basis) and in-stream mode (in real or near real-time, through action triggers ), when the project requires quick responses and update management of various data streams.

For example, with the Lambda function, we can carry out the processing of sales transactions in a multinational company, determining in which storage plant the order must be carried out, allowing the continuity of the complementary process workflow.

Benefits of using Amazon S3 for Data Lake

Using Amazon S3 for a Data Lake allows us to have high scalability, excellent costs, and an adequate level of security, thus offering a comprehensive solution to carry out different processing models.

When the data is stored in S3, we can use the AWS Glue service to create a data catalog that users can query. The process can be complicated when it comes to monitoring data flows, configuring access control, and defining security policies.

At Nubersia we advise you on the process of migration to the cloud with AWS as well as design and implementation of Data Lake and analysis tools for your organization. Did you know the possibilities that AWS offers you to create a Data Lake