Streamlined Data Processing: A Guide to Cost-Effective ELT Implementation

In a world driven by data, every enterprise needs an efficient system for gathering, moving, and transforming data for analysis. The process is vital for business growth and innovation.

Without an efficient system in place, enterprises have to tirelessly struggle to make sense of the vast amount of data available to them. Also, with data scattered on multiple systems, companies cannot obtain a 360-degree view of their business. Therefore, they need data integration to bring all data together, creating an uninterrupted flow of data from multiple sources to a target system.

One common model for data integration is ELT, i.e. Extract, Load, and Transform. It is the process of extracting data from one or more sources and directly loading it into a data warehouse for analysis. The goal of ELT implementation is to enable enterprises to derive valuable insights and make data-driven decisions.

Considering cost-effectiveness in ELT implementation is equally significant as it ensures efficient resource allocation, scalability, performance, ROI, and data quality. By considering cost-effectiveness throughout the ELT process, enterprises can optimize their data management practices and derive maximum value from their data assets.

In today’s blog, we will discuss ELT implementation as well as understand the significance of cost-effectiveness in the same.

Understanding ELT

A lot of you must already be familiar with ETL, i.e. Extract, Transform, and Load. ETL has been the conventional process for integrating data for many years now. The first ETL tools emerged in the 1990s when there were just a few operational systems, mostly structured data, and gradual changes in insights.
However now, with the vast amounts of data available, there is a growing need for quick and meaningful insights. This need has resulted in the requirement for a quicker and more efficient data integration process.

ELT (Extract, Load, and Transform) is an advancement of ETL. It is a modern approach that eliminates the need for a middle-tier server by deferring the data transformation step to the last.

Here’s what the ELT process looks like:

Nowadays, most business enterprises are opting for ELT implementation as the process better suits modern-day cloud data warehouses. With ELT, these enterprises can benefit from the incredible processing power and storage capabilities of data platforms available nowadays and easily handle massive volumes of data.

ELT implementation allows data engineers to conduct data transformations in parallel, thus securing better performance and quicker time-to-insight.

Additionally, ELT separates the extraction and loading steps from the transformation step. The extracted data is loaded into a target system without immediate transformation, which is only performed at the time of analysis or when generating views. This means that when modifications are required, they can be made simply by adjusting the code that generates the view, without needing to modify the entire data pipeline. This provides a significant advantage in terms of speed.

Cost-Effective ELT Implementation

Now that you have understood the key differences between ELT and ETL, you will know that ELT is a key component of Modern Data Stacks.

If your enterprise is also looking to ensure cost-effective ELT implementation, you must choose the right ELT tool. Here, we will discuss the most crucial factors to consider while choosing an ELT tool or platform for your enterprise.

• Data Sources and Destinations ELT tools require destinations where you can store your business analytics data. These destinations typically include data warehouses such as Amazon Redshift, and Snowflake, or data lakes such as Microsoft Azure, Amazon S3, etc. Certain ELT tools allow you to store data in one data warehouse only whereas others allow multiple destinations.

Additionally, make sure your chosen tool provides connectors or integrations with the data sources you use, such as databases, cloud storage services, APIs, or streaming platforms.

• Performance and Scalability
  It is important to consider the scalability of the ELT tool. For instance, will it be able to handle increasing data volumes and growing workloads? Also, evaluate the performance of the tool by assessing parameters such as data processing speed, support for distributed computing, and parallelization. Checking these factors will make sure you select the right tool that suits your business requirements.

• Usability
  Check the simplicity and user-friendliness of the ELT tool. Make sure the tool allows setting up integrations easily and supports data replication on different schedules. The flexibility and customization of the tool should be such that it makes your business more productive.

Don’t forget to consider factors such as the intuitiveness of the user interface, ease of configuration and deployment, availability of documentation and support resources, and the learning curve for your team members.

• Transformation Capabilities
  Every ELT tool comes with its own set of transformation capabilities. For instance, it may include features such as data cleansing, data filtering, and aggregation, schema evolution, etc. Make sure your team can easily write and maintain transformation logic for the chosen tool. Also, check whether the tool allows complex data manipulations and transformations required for your specific business use cases.

• Data Governance and Security
  Assess the data governance and security features of the ELT tool or platform. Consider factors such as data lineage, metadata management, data cataloging, access controls, encryption, and compliance with data protection regulations (e.g., GDPR, SOC2, and HIPAA). Make sure all security controls are user-configurable.
  Lastly, ensure that the chosen tool aligns with your organization's data governance policies and security requirements.

• Pricing
  The pricing of ELT tools may be based on different factors such as the amount of data replicated, the number of data sources, etc. Once you know your requirements, check out the pricing plans offered by different vendors. Evaluate if a pricing plan aligns with your budget and your usage patterns.

Additionally, check if there are any additional costs involved, such as maintenance fees, support fees, or costs associated with data processing.

Customer Support

You must also evaluate the level of customer support available around the ELT tool. Check factors such as technical support channels, frequency of updates, forums, etc. to establish customer support. This is a crucial factor that comes in handy when you need assistance or face any troubleshooting issues.

By considering these factors, you can make an informed decision when choosing an ELT tool that best fits your enterprise’s specific requirements and goals.

Best Practices for Streamlined Data Processing with ELT

Streamlined data processing with ELT involves implementing some best practices to optimize the efficiency and effectiveness of your data pipelines.

Here are some of the best practices that you must integrate:

• Thorough Evaluation of Data Source
  It is crucial to understand the type and volume of data that your ELT tool will be handling. For this, it is important to thoroughly evaluate the data source. For instance, data sources may include SaaS applications, databases, flat files, etc. The type of source will determine the data type, schema of data, and other details.
  Make sure to carefully evaluate your data sources. Understand the structure, format, and quality of the data to ensure compatibility and identify any potential issues or challenges that may arise during extraction and loading.

• Address the Data Issues
  Data processing is a vital business activity. To ensure its effectiveness, every enterprise must solve any data issues that may arise in one of the runs of the ELT cycle. This will help to prevent the repetition of the same error again and again.
  Some of the best ways of addressing data issues include adding autocorrect tasks or adding data validation constraints.

• Leverage Data Quality Checks
  Another one of the best ELT practices is implementing data quality checks to identify and manage data inconsistencies, missing values, or errors. You must validate data integrity and perform data profiling to ensure the availability of high-quality data for analysis. It is also advisable to apply data cleansing techniques.

• ELT Logging
  ELT logging is the process of recording every event that takes place before, during, and following an ELT process. It helps to capture and handle any data processing errors or exceptions that may occur during the ELT process. Detailed logging also facilitates troubleshooting and provides insights into potential issues.

• Use Parallel Processing
  Parallel processing is a brilliant way to distribute the workload across multiple resources. The process enables you to categorize the tasks into smaller units and process them concurrently. This will help to reduce the overall processing time.

• Set up Checkpoints for Recovery
  It is advisable to set up checkpoints throughout the ELT process. Many times, unexpected errors or failures can occur while moving large amounts of data. With checkpoints, enterprises can keep track of where the error occurred so that there is no need to restart the process from the very beginning.

• Incremental Data Extraction
  Implement incremental data extraction strategies to only retrieve and process the changed or newly added data since the last extraction. This approach minimizes the amount of data processed, thus reducing the overall processing time and resource requirements.

By following these best practices, you can streamline your data processing with ELT, improve efficiency, and enhance the overall quality of your data for analysis and decision-making purposes.

Dive Deeper Into ELT Cost-Effectiveness

To conclude, ELT presents a better and faster approach to data pipeline management. It can help enterprises defer the data transformation step to the data store and benefit from efficient data processing and quicker analytics. Besides, it can also handle massive volumes of data.

If you wish to simplify your data pipeline management, you must get in touch with the experts at Algoscale. As one of the leading data engineering companies, we can help you make data integration and transformation simple with our robust and customized ELT pipelines. We will assess your precise business requirements and build an advanced solution that ensures your business data is exactly where it needs to be.