A Step-by-Step Guide: How to Build Data Lakes in Data Engineering

data-sql-markus-spiske-hvSr_CVecVI-unsplash-min

According to the IDC, the global data sphere will grow from 33 zettabytes in 2018 to 175 zettabytes by 2025.  In the face of this exceptional growth, the skillful collection, management, and analysis of data is becoming crucial for success in today’s business environment. Hence, data lakes are growing into a powerful tool. In this guide, we take a step-by-step look at building a data lake in data engineering.

Data lake – definition

The data lake is where all your company’s data goes. Data can be structured, semi-structured, or unstructured for future use. What’s more, files placed in such a cloud can be used many times. Thus, it is not necessary to create additional places to re-analyze the data.

7-step guide for building data lakes in data engineering

Dealing with SQL problems

STEP 1: IDENTIFYING THE DATA SOURCE

This is the foundation of the entire process of building a data lake. In this step, we identify a variety of data sources from which we will draw information to enrich our data lake. They may include different types of data, such as:

  • Data from operating systems, business applications
  • Data streams
  • CSV files
  • Database
  • APIs, and much more

Understanding where your data comes from is critical to ensuring the accuracy and consistency of information in your data lake. It is the data sources that will form the basis of any analysis and processing in the future, along with efficient data engineering services. In addition, the identification of data sources allows you to avoid later problems.

STEP 2: INGESTING DATA

Data ingestion is the process of moving data from previously identified sources into a data lake. In this step, data is physically retrieved and stored in a data lake structure. This process can involve both structured and unstructured data, such as text files, photos, and multimedia.

It is a key step that provides the base material for further activities in the areas of data analysis, processing, and visualization. The correct implementation of this stage has a significant impact on the quality and timeliness of the data in the lake.

STEP 3: INTEGRATING DATA SOURCES

Data source integration is the process of combining and transforming data from different sources to create a cohesive and consistent set of data in a data lake. In this step, you can transform and standardize the data to make it understandable and useful for analysis.

It is essential as different sources provide data in different formats, structures, and qualities. Without proper integration, data can be difficult to analyze and compare.

STEP 4: ORGANIZING DATA

Data organization is the process of structuring, categorizing, and ordering data in a data lake. In this step, appropriate directory structures, metadata, and indexes are created.

Well-organized data makes it easier to manage the data lake, search, and maintain consistency and ease of access. Creating meaningful structures makes it easier for analysts and end users who use data.

STEP 5: PREPARING DATA

Data preparation is the process of transforming, cleaning, and enriching data. It ensures the data’s quality, consistency, and suitability for further analysis. In this step, the data is aligned with analytical and business requirements.

Data in its original state may contain errors, omissions, ambiguities, or incomplete information. Data preparation removes these problems.

STEP 6: VISUALIZING DATA

Data visualization is the process of creating charts, graphics, diagrams, and other visual forms. It helps understand, analyze, and present the collected data in a data lake. Visualizations enable users to transform raw data into intuitive and accessible information.

The human brain is more likely to understand and remember information in visual form. So, visualizations allow you to

  • Draw quick conclusions
  • Identify patterns and trends

STEP 7: COMPLIANCE WITH REGULATIONS

Regulatory compliance is the process of ensuring that data stored and processed in a data lake complies with applicable laws and regulations regarding privacy and data security. Improper data processing can have significant legal and reputational consequences.

Data lakes tools

As the volume and variety of data grow, data lake tools provide critical support for organizations. Below there are some data lake tools that enable businesses to build scalable data storage and analytics solutions:

  • Infor Data Lake is a platform that allows you to collect and analyze data. It creates consistent data sources in a data lake.
  • Snowflake is a flexible cloud database ideal for data lakes. It offers scalability and real-time analytics.
  • Google Cloud Storage. This is a service perfect for creating and managing data lakes.
  • AWS Lake Formation. It helps build and manage data lakes in the cloud with a focus on regulatory compliance.
  • Azure cloud storage service provides scalability and security for data lakes.

Summary

To sum up, building a data lake in data engineering is becoming an indispensable tool in the era of huge amounts of information. With well-thought-out steps and the right approach, organizations can effectively collect, manage and fully use data. And this has a significant impact on achieving business goals in today’s world.

Other articles from totimes.ca – otttimes.ca – mtltimes.ca

Facebook
Twitter
LinkedIn
Email
Ottawa

The latest on what’s going on in your city – delivered straight to your inbox