Best Database ETL Guide for 2023

Data is an important component for every business which makes Database ETL integral to Data Analytics. It is a rich source of information that can help businesses make sound decisions. However, for a business to extract information from data, it must analyze it. The problem is that most data sources are not optimized for analytics. 

This means that businesses should extract data from such sources and move it to a tool that is optimized for analytics. In most cases, this is a Data Warehouse like BigQuery or Snowflake. The ETL process helps businesses integrate data from multiple sources into a Data Warehouse. 

Table of Contents

• What is Database ETL?
• Why do we need Database ETL?
• How does Database ETL Work?
• How to Replicate Data from a Database to Another Database or a Data Warehouse?
• Challenges in Database ETL
• Best Database ETL Tools in 2023
• FAQs
  • What are the types of Databases in ETL?
  • How many steps are there in a Database ETL Process?
• Conclusion

What is Database ETL?

ETL refers to the three steps (Extract, Transform, Load) used to integrate data from multiple sources. It’s the common process used to build a data warehouse. During the ETL process, data is taken (extracted) from the source system, converted (transformed) into a format that is easy to analyze, and stored (loaded) into a data warehouse or another system. The exact ETL steps may differ from one tool to another, but the end result is the same. 

ETL solves two major problems to enable better analytics:

1. Data analysis can be performed in an environment that is optimized for that purpose: Transactional database management systems like PostgreSQL and MySQL are good for processing transactional workloads. They are good at reading and updating single rows of data with low latency. However, they are not good at conducting large-scale analytics across huge datasets. 

2. Cross-domain analysis: When business leaders join data from multiple sources, they can answer deeper business problems. This demand becomes more urgent as businesses become more complex and deploy systems in the cloud. 

For more details, you can refer to our blog.

Why do we need Database ETL?

Here are a few reasons why ETL is still considered a pivotal process by enterprises:

• Database ETL generates an easy path for businesses to analyze and provide the data relevantly on their assigned initiatives.
• Database ETL also has the potential to provide historical context for your organization when used in tandem with the data at rest within the Data Warehouse.
• Database ETL also provides support for the upcoming interaction requirements.
• You can leverage Database ETL to migrate data without any technical skills. Therefore, it can help improve the productivity of the team dramatically.
• Database ETL is deemed one of the essential tools for an organization with its Data Reporting, Data Warehousing, and analytics tools.

How does Database ETL Work?

Previously, this process would have extracted data from one or more OLTP (Online Transactional Processing) databases. OLTP Applications generally consist of a high volume of transactional data that needs transformation and integration to operational data. This is needed because it can come in handy for Data Analysis and Business Intelligence.

The data gets extracted on to a staging area, that serves as a storage location that sits between the data source and the data target. Within that staging area, these tools can modify the data by cleansing, joining, and otherwise optimizing it for analysis.

This tool can then be leveraged to load this data into a Decision Support System (DSS) database, where BI teams can run queries and show results and reports to business users to help them make decisions and strategies.

However, since the traditional tools still require a considerable amount of labor from data professionals, this is where the modern tools jump into the fray. You can easily analyze data from pre-calculated OLAP summaries, which helps ease and speed up the process.   

How to Replicate Data from a Database to Another Database or a Data Warehouse?

For replicating data from one database to another database or data warehouse manually, there are several Database ETL methods. You can either download and upload CSV files, write custom scripts, use the UI of the database management system or simply automate the whole process by using cloud-based ETL tools . For instance, if you want to replicate data from MySQL to Oracle, you can simply use the MySQL Server application. Similarly, for replicating MariaDB to Amazon Redshift you can upload data as CSV files by writing SQL commands. These methods are an effective choice if this is a one-time replication process with little to no data transformations required.

For cases, when you need fresh data every few hours from multiple sources and perform complex transformations on it to make it analysis ready, then wrting custom scripts may not be most effective choice as it will require a lot of time and effort from your engineering team. They will need to constantly monitor all the data connectors and look out for any data leakages that they need to fix. As a more economical and effortless solution out of this, you can be try out no-code ETL tools like Hevo Data that completey automates the data integration process.

Challenges in Database ETL

Here are the challenges that you’ll encounter during the Database ETL process:

• Scalability: Scalability is pivotal to the functioning of a modern tool. The amount of data being collated by businesses is only going to go up. You might be resorting to batch migration for now, but as your business evolves, you might need to adopt Streaming Replication. This is where the Cloud jumps into the fray.
• Accurate Transformation of Data: Another challenge you might face is accurate and complete data transformation. Coding or manual changes and a failure to test and plan before running a Database ETL job can sometimes introduce faults, loading replicas, including missing data, among other issues. A Database ETL tool can help you reduce the need to hand-code and decrease the occurrence of errors drastically. You can also use Data Accuracy testing to identify inconsistencies and duplicates. You can use monitoring features to identify instances where you are dealing with incompatible data types among other Data Management issues.
• Diverse Data Sources: Data continues to grow in volume and complexity. One company might be handling diverse data from multiple data sources that consist of structured and semi-structured sources, streaming sources, FLAT files, etc. Some of this data can be easily transformed in batches, while for others streaming transformation might be suggested. Handling each type of data in the most practical and effective manner might pose an enormous challenge.

Best Database ETL Tools in 2023

There are several popular Database ETL (extract, transform, and load) tools that can be used to extract data from databases, transform it, and load it into another database or data warehouse. You can select a database ETL Tool apt for you depending on your use case, the number of data sources, data replication frequency, data volume, and pricing. Database ETL tools like Hevo Data, Airbyte, Stitch, etc., are widely used for connecting multiple data sources and loading them to a destination of your choice, such as a data warehouse. You can check out the article on the best ETL tools to know which tool suits you the best.

FAQs

What are the types of Databases in ETL?

The different types of databases that can be leveraged in Database ETL are as follows:

• NoSQL Databases
• Cloud Databases
• Relational Databases
• Wide-column Databases
• Columnar Databases
• Key-value Databases
• Object-oriented Databases
• Graph Databases
• Hierarchical Databases
• Document Databases
• Time Series Databases

How many steps are there in a Database ETL Process?

The 5 steps that comprise the ETL process are as follows:

• Extract: In this step, you extract raw data from multiple disparate sources, which is then moved to a temporary staging data repository.
• Clean: In this step, the raw data gets cleaned, ensuring the quality of data before transformation.
• Transform: In this step, the data is converted and structured to match the correct target source.
• Load: Here, the structured data is loaded into a Data Warehouse so that it can be properly analyzed.
• Analyze: In this step, Big Data is processed within the Data Warehouse, allowing the business to gain insight from the properly configured data.

Conclusion

This is what you’ve learnt in this article:

• What is the Database ETL process.
• What is involved in each phase of the ETL process. 
• How to choose an ETL tool. 

If you’re looking for a more straightforward solution, you can use Hevo Data – a No Code Data pipeline to build perform Database ETL in an instant.

Hevo has pre-built integrations with 100+ sources. You can connect your SaaS platforms, databases, etc. to any data warehouse of your choice, without writing any code or worrying about maintenance. If you are interested, you can try Hevo! sSign up here for a 14-Day Free Trial!

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