Is it even worth it to create custom ETL anymore?

The concept of Big Data has been around for a while, yet many still struggle with how to properly structure and store it. Let’s explore the differences between Data Lake and Data Warehouse, as well as what ETL means.

Data Lake vs Data Warehouse

Data Lake and Data Warehouse are both used for aggregating multiple data sources, but they serve different purposes and have different architectures.

A Data Warehouse (DWH) is a system that consolidates data from various sources into a single, centralized, and consistent storage solution. Its primary goal is to support data analysis, data mining, artificial intelligence, and machine learning, accommodating large volumes of data from all departments within an organization. In contrast, a Data Lake offers an alternative approach to storing and processing large volumes of data. Unlike a Data Warehouse, where data is pre-structured within a relational model and follows a defined schema, a Data Lake provides a space to store any type of data—structured (like relational database tables and Excel files), semi-structured (such as XML or JSON files, web pages), and unstructured (including images, audio files, and social media posts). This approach allows for the preservation of raw data without prior processing, offering flexibility for subsequent analysis.

Is it even worth it to create custom ETL anymore?
Is it even worth it to create custom ETL anymore?

What is ETL?

ETL (Extract, Transform, Load) is a data integration process used to extract data from various sources, transform it into a consistent format, and then load it into a Data Warehouse for analysis and reporting.

ETL data sources can vary widely, including:

Databases: SQL servers, Oracle, MySQL, MongoDB. CRM systems: Salesforce, HubSpot. CSV, Excel, XML files. Web services: APIs, web scraping.

The ETL Process:

  • Step 1 - Extraction
  • Step 2 - Transform
  • Step 3 - Loading

Let's break down the ETL process with an example for a retail company:

  1. Extract: This begins with data input from various CRM systems, including customer information such as names, email addresses, last purchase dates, and amounts spent.
  2. Transformation: During this phase, data is standardized. For example, names are converted to uppercase, email addresses are transformed to lowercase, dates are formatted as DD-MM-YYYY, and amounts are displayed in two decimal places. Several business rules dictate how data should be transformed, such as filtering out records that don’t meet certain criteria or merging data from different sources.
  3. Loading: Finally, the data is loaded into the company's data lake, with expectations for daily updates. As a result, the company receives clean and standardized data that can be used for analyzing purchases and planning marketing campaigns.

Restaurants and cafes can use ETL processes to optimize delivery by integrating data from various sources such as their own websites and delivery platforms. This allows them to effectively manage couriers, reduce delivery times and improve customer experience.

An alternative approach is ELT (Extract, Load, Transform), where data is first extracted, then loaded into a Data Lake, and subsequently transformed into the required format.

Data Lake vs Data Warehouse
Data Lake vs Data Warehouse

Popular Modern ETL Tools in the Market

Thanks to those tools you can monitor workflows, also they provide data integration, data quality, and master data management.

  • Airflow
  • Airbyte
  • DBT
  • Prefect

Why Custom ETL is Essential for Companies:

Companies may need custom ETL solutions when:

  • They have unique data sources or integration requirements that off-the-shelf ETL tools cannot meet.
  • They require full control over the ETL process, including data quality, transformation logic, and error handling.
  • They have high security and compliance requirements that cannot be fulfilled by cloud-based ETL tools.
  • They need to expand or customize existing ETL pipelines.

Is it even worth it to create custom ETL anymore

Absolutely. By offering ETL services, we help clients not only integrate data from various sources but also ensure its high quality, which is critical for making informed business decisions. Given the diverse data sources and processing requirements, ETL remains a vital tool in a business's arsenal for achieving analytical efficiency and supporting strategic initiatives.

Need help building a data warehouse? Take advantage of CODECAVE expertise

CODECAVE is IT service provider that has got a wide range of tech expertise including big data analytics, data science, AI & ML, cloud solutions, Custom plugin development for Autodesk AutoCAD®, Revit®, Interactive 3D visualization, AR, and VR experiences that captivate users, DevOps, and much more. Our software development team helps to build solutions for businesses in fintech, retail, food industry, media, automotive, healthcare, and other industries. We help many clients harness their warehouses and data lakes, tackle their Big Data, improve business intelligence, and gain the maximum value from all of it.

For example, as a part of cooperation with Italian foodservice industry's company (under NDA), the CODECAVE team has built the data warehouse system for storing and processing significant amounts of data. The team implemented a data categorisation system, which was implemented using machine learning. This enabled the data to be loaded section by section into a warehouse. The warehouse system allows the company to receive timely reports and provides the basis for operational processes.

If you have any questions about ETL process or its potential value for your business, contact our team for free consultation.