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ETL BEST PRACTICES: TIPS FOR EFFICIENT DATA EXTRACTION, TRANSFORMATION, AND LOADING

ETL BEST PRACTICES – INTRODUCTION

Everyone reading this blog must agree that Data engineering is a vast domain nowadays with the growing amount of online and offline data. With the growing online data flow, fetching data from multiple sources to one place is a considerable challenge. The data integration process of extracting data from multiple sources, transforming data, and loading it into the destination database is called the ETL process. Here we are discussing ETL best practices to follow.

ETL stands for Extract, Transform, and Load in the data engineering businesses.

Extract data from the source.

Transform the data into a suitable format.

Load the data to the target database.

Top 10 ETL Best Practices

UNDERSTAND THE PROJECT REQUIREMENTS.

One of the most essential parts of the ETL best practices is a clear understanding of business requirements. The organizations analyze the available data using business analytics tools, which help to extract a broad range of data sources and types.

Data Source and target analysis – Analysis of how the data gets produced and in what format the data needs to be stored.

Usage and Latency – Analyzing how the data will be loaded at the target database and how the target users will consume it.

AUDIT OF DATA SOURCE

An audit of data sources includes assessing the information quality and usefulness of the available data for fulfilling the business requirement. Data auditing includes data profiling and assessing poor-quality data and its impact on organizational performance.

DETERMINE DATA EXTRACTION APPROACHES

The main objective of the ETL process is to extract all the required data from the source seamlessly. Hence the data engineer must be conscientious.....

Read full blog @ https://www.64-squares.com/etl-best-practices-tips-for-efficient-data-extraction-transformation-and-loading/

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INTRODUCTION TO DATA ENGINEERING: WHAT IT IS AND WHY IT MATTERS

INTRODUCTION 

Data, data, and data. Go to the internet and search about data engineering, you will realize nowadays the concept of data engineering is been in the center of discussion. Massive amount of data is getting generated every day in most businesses today. 

This may include the data generated out of the stock market apps, customers’ responses, everyday sales performance, website/app analytics data, and many more. 

This information is crucial for the business to perform well. And hence needs to be analyzed professionally. Now let us understand what’s the DATA. 

WHAT IS A ‘DATA’? 

Data is information stored in a format that is efficient for movement and processing. Data may be stored in different types like video, audio, images, and text. Every data is stored in the computational system in a binary format. Raw data is nothing but data in its most basic digital format. 

In the recent few years, we can say data in the process of business analytics has gained importance. Data processing has become popular and important nowadays, in the field of cloud computing.

DATA ENGINEERING – OVERVIEW 

Data engineering is the field where data engineers build a software system to collect, process, store, and analyze data at a large scale. Organizations nowadays generate a massive amount of data. They need the right people and the right technology to process the data in usable outcomes which helps in the organizational decision-making process. 

Data engineers work in a variety of settings to build data processing systems that collect manage and convert raw data into meaningful information for data scientists and business analysts. As we all know, engineers design and build things, the same way, data engineers design and build data pipelines to optimize data into usable information for data scientists and business analysts.

Read full blog @ https://www.64-squares.com/an-introduction-to-data-engineering-what-it-is-and-why-it-matters/