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.
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.
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.
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 while designing an ETL process. Also, data engineers must be careful regarding response time and performance critical metrics of the implemented ETL process.
Data extraction is the most initial and essential phase of the ETL process. This phase of ETL is all about pulling data from sources such as databases, applications, web pages, and files. Below are the standard procedure for data extraction.
Identify the data sources: – Determine where the data comes from and the data sources needed for extraction.
Connect to the data sources – Establish connections to the data sources such as databases, websites, and files.
Define the data to extract: – Identify the data that needs to be extracted and retrieve it from the sources.
Filter the data – Here, we apply filtration logic to the data to remove any errors, unwanted data, or records.
Cleanse the data: – At this step, we ensure that the extracted data is free of errors, inconsistencies, or duplicates.
Validate the data: – Check the extracted data against existing business rules or constraints.
Extract the data: – The final step is to extract the data from the sources and store it in the target system, such as a data warehouse or a data lake.
Define your data cleansing requirements: – We must understand the needs. Data cleansing requirements depend on the data type, format, and the issues you must address.
Identify the data cleaning techniques to use:- Data cleaning techniques may include removing duplicates, filling in missing values, correcting invalid or inconsistent data, and dropping unnecessary columns, among others.
Choose the correct programming language:- Choose a programming language that supports your cleaning techniques. Python is preferred, but you can g for R, SAS, and SQL as required.
Import your data: – Import your data from multiple sources into your chosen programming language.
Write the code for cleaning your data: – With the help of your chosen programming language, write functions and scripts to carry out your cleaning techniques.
Test your data cleansing module. – Test your data cleansing module against the sample database.
Deploy your data cleansing module:- Deploy it on the production environment once the module testing is complete.
Monitor your data cleansing module: – We need to monitor the module to check if it functions correctly over time.
ETL logs are an important part of the overall ETL process. ETL logs are important in troubleshooting, auditing, performance optimizations, and analysis. ETL logs are important, especially in long-running ETL jobs where it is important to track progress and identify any issues that arise.
In today’s fast-paced business, data has become a crucial part of almost any business you name. Having clean and correct data in the system has become essential to mark corporate profit or loss. Hence resolving data errors either manually or automated is required.
Setting up the recovery points at intervals is the best practice in the ETL process. It allows the engineers to resume the ETL process from the recovery point instead of starting the whole process again.
Modulerising the code enables the data engineers to back-trace the issue to the origin. Building code modules makes error handling easy for engineers. Modules are nothing but code blocks managed to perform together or sequentially to reach the business requirement. It also helps improve code readability.
It is a dedicated data preparation area where all the necessary data is dumped. The data may be in the form of XML files, flat files, relational databases, etc. This is a staging area where you can perform all tasks like sanity checks, data cleaning, and correcting.
Setting up error alerts helps in the timely resolution of errors happening in the ETL process. Setting up error alerts is essential due to a few reasons as below:
Incorporating ETL Best Practices is crucial for ensuring smooth, efficient data workflows that deliver accurate and actionable insights. By focusing on data quality, performance optimization, and security, organizations can establish a robust ETL pipeline that scales with business needs. Following these ETL Best Practices not only enhances data reliability but also supports more informed decision-making across the enterprise.
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