The Importance of Custom ETL Solutions: Extract, Transform, Load

This blog post emphasizes the advantages of custom ETL solutions and the need for careful consideration in each phase of the ETL process.

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The Importance of Custom ETL Solutions: Extract, Transform, Load

Is it truly necessary to develop personalized ETL solutions when pre-built tools are readily accessible? This is often the initial query decision-makers pose, either internally or to their team leads. The typical response leans towards affirmation due to the remarkable advantages in terms of unmatched adaptability, efficiency, long-term cost-effectiveness, and authority over data integration and transformation procedures. In this article, we will delve into different aspects concerning Custom ETL Solutions, encompassing their merits and obstacles, factors to ponder, and strategies for implementation.
The ETL process comprises three pivotal components: Extract, Transform, and Load. Notably, Transform and Load can be employed interchangeably, a choice influenced by the specific use case. Therefore, even in the context of ETL, it's essential to recognize it as ETL/ELT.

Let's begin our discussion with the "Extract" phase:

The extraction of data from diverse and disparate sources poses an enduring challenge within the realm of data engineering. This data can take various forms, spanning structured, unstructured, and semi-structured formats, and it resides in a range of systems, including:

  • Platforms or tools tailored to address issues within HRMS, ECommerce, LMS, ERP, POS, DMS, and beyond.
  • Databases.
  • APIs and/or web services.
  • Flat files.

There exists a selection of ready-made platforms, such as Fivetran, Airbyte, Dataddo, Segment, and others. These platforms offer connectors that facilitate the retrieval of data from sources and their consolidation into a centralized database or destination.
Before delving into custom solutions, several complex inquiries should be raised, such as:

  • Assessing the necessity of data "extraction" from sources versus the feasibility of situating your platform/system in proximity to the data sources when working with databases, data warehouses, or data lakes.
  • While employing third-party platforms like Fivetran or Airbyte, it's essential to ascertain if they cover all the data sources required, both presently and in the foreseeable future.
  • Verifying compatibility with desired destinations, both current and prospective, and evaluating the cost-effectiveness of these platforms.
  • Evaluating the time taken for benchmarking against data sources or datasets, and deciding if waiting for a certain duration is acceptable.
  • Contemplating whether investing in a bespoke fetching solution is a more robust and controllable option.
  • Determining if incremental data fetching is supported.
  • Examining how well these platforms adapt to schema changes.
  • Investigating alternative solutions like Databricks' Federation Lakehouse, which provides querying, reading, and writing capabilities irrespective of the underlying databases or data warehouses. Could such solutions be a more suitable choice?

In essence, addressing the extraction phase necessitates careful consideration of existing tools, their capabilities, and the possibility of tailored solutions that align with your data integration requirements.

Now, let’s talk about the “Transform” phase: 

Irrespective of the option chosen within the "Extract" phase, carrying out this stage is essential due to various factors:

  • Inaccurate Data Input: Data input introduced by human sources into systems is prone to errors.
  • Dealing with Duplication: Managing the deduplication process is crucial.
  • Custom Attributes and Fields: The addition of new attributes or fields to accommodate novel requirements.
  • Data Collation: Aggregating multiple datasets into unified time series or datasets.
  • Supporting Visualization Layers: Enabling data visualization across different scales, from a comprehensive view to a detailed one.

There are off-the-shelf platforms available to support data transformation: Talend, Matillion, Informatica, etc, however, as highlighted earlier, the decision to undertake this phase hinges on the specific use case. The design of use cases should take into account:

  • Cost and Data Volume: Large data sizes entail substantial resource usage and costs.
  • Processing Time: Determining whether real-time transformation or batch processing is necessary.
  • Utilizing Data Lakes or Warehouses: Temporary or intermediary steps stored in data lakes or warehouses aid in debugging and contribute to data lineage.
  • On-Premises Limitations: Considerations about the limitations of on-premises infrastructure.
  • Cloud Services 
  • Availability: Ensuring that cloud services are accessible in required regions and on demand.
  • Scalability: The capability to scale in response to varying loads.
  • Affordability: Staying within budget constraints while considering the costs.
  • Security: Safeguarding against potential vulnerabilities posed by malicious actors.
  • Data Integrity: Maintaining data integrity when interfacing with various systems or platforms within a private cloud environment.

In essence, the necessity of the "Extract" phase emerges from practical considerations and the alignment of technical decisions with the overarching objectives of the data integration process.

And the “Load” phase:

The triumph of ETL/ELT processes pivots on the efficacy of this phase, as any subsequent endeavors cannot proceed if data fails to be deposited into a central system. Before executing this stage, it's imperative to address pivotal objectives:

  • Selectively Load Necessary Data: Extract and load solely the pertinent data from diverse sources or platforms.
  • Comprehensive Data Comprehension: Grasp the nature of the data and load it into the target destination following a standardized approach.
  • Facilitate Data Transition: Enable the easy transfer of data to/from archival or glacier stages to regulate costs.
  • Attribute Identification: Possess the capability to discern between raw and derived attributes, facilitating debugging efforts.
  • Exception Handling Proficiency: Be adept at managing exceptional scenarios during the data loading process. 

By diligently addressing these objectives, the "Load" phase maximizes the potential for successful ETL/ELT execution, ensuring data is harmoniously integrated into the central system for subsequent processing and analysis.
Our forthcoming blog post will delve into the implementation strategies and benefits associated with customized ETL solutions.

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