About 59 petabytes of data were created and consumed every single day in 2021. To put that in perspective, just one petabyte is equivalent to 500 billion pages of standard printed text. It is estimated that the volume of data generated will double in the next two years. This brings us to an important question; are businesses using the right tools to leverage this data boom? Enterprises using Data Warehousing and Big Data solutions see 8-10% jump in profits. Statistics say that stored data grows 5x faster than the world economy and poor data quality costs just the US economy a staggering $3.1 trillion a year. In this scenario, a robust data warehousing solution is key to driving successful business strategies.
Organizations of all sizes are adopting modern data warehouse technologies to increase the scale of data ingest and analytics to stay relevant in the data game. Enterprises are looking to embrace data warehouse solutions and open-source tools that have serverless capabilities, in-memory analytics, performance optimization for big data, multi-region and multi-cloud strength. Advanced data warehousing tools serve as the backbone of modern cloud applications by delivering flexibility, scalability, durability and correctness.
What is ELT and how does it work?
ELT is a data integration process that extracts, loads and transforms data. In short, it is the process of extracting large volumes of data from multiple sources, loading it into a target data warehouse and then transforming data for analytics by cleaning and organizing it.
Extract: Data is extracted from a source to a staging area, which acts as a buffer between the target data warehouse and source data. Data comes in from multiple sources and in various formats.
Load: Raw data is directed to the data warehouse or data lake from where data can be transformed as needed. Data lakes acts as a central store designed to hold both structured and unstructured data at a massive scale.
Transform: Data cleansing and organizing takes place in the transformation stage. Raw data is normalized, converted to single system format and organized by sorting, filtering, validation, deduplication and summarization of data. This improves data quality and compliance.
ELT (extract, load, transform) is an alternative to the traditional ETL (extract, transform, load) process. The difference between the two is that the data is transformed before loading to the data warehouse in an ETL process. On the other hand, ELT is a more modern approach which enhances performance and is useful while processing massive data sets needed for business intelligence and big data analytics. Many enterprises are switching to ELT due to increase in use of unstructured data and cloud-based storage systems.
Designing a successful ELT process:
The key to having a successful ELT data warehousing is to pay close attention while designing your ELT process.
Accurate logging: It is important to ensure your data system accurately logs new information. It is vital to audit data after loading to check for lost or corrupt files. By following this procedure, enterprises can debug ELT processes if any integrity challenges arise.
Diverse sources: ELT processes must be equipped to handle all forms of structured and unstructured data as some of the information gathered from sources may lack the data structure required for analytics.
Stability: Build a fault-tolerant data warehouse system which can recover after a shutdown. It is not uncommon for ELT pipelines to get overloaded and run into problems. When such a scenario arises, having a robust data system can help enterprises move data without it getting lost or corrupted.
Alert system: Incorporate an alert system that notifies potential problems within an ELT process to ensure accuracy of business insights. Design a system to trigger alerts for expired API credentials, third-party API bugs, connector or general database errors and more.
Data latency: It is important that business intelligence platforms have up-to-date information to deliver accurate insights. Therefore, enterprises must work towards reducing data latency while building an ELT process.
Flexibility: Choose the right ELT tools and solution that is flexible to scale up and down according to your enterprise needs. A flexible solution will also help you save cost on cloud-server processing and storage fees.
Benefits and use cases of ELT data process:
- The primary advantage of ELT is the flexibility and the ease in which companies can store new, unstructured data.
- ELT can save data in any format, even if enterprises do not have the ability to transform the data, thus providing immediate access to all the information whenever one needs it.
- By separating the loading and transformation tasks, ELT minimizes the interdependencies, thus lowering risk and streamlining project management.
- ELT is a faster option as it allows all data into the system instantly and users can determine if they need to transform the data.
- ELT is cloud-based and leverages automated solutions instead of relying on manual updates. The solution is easy maintenance and saves costs in the long run.
ELT data processes are generally preferred in environments where high volumes of data are involved or where real-time data access is needed. Enterprises make the transition from ETL to an ELT data architecture due to increase in the amount of data they are dealing with or their product or service needs real-time response and interaction. Examples of immediate access of real-time data include stock exchanges, large-scale wholesale distribution of stocks, industrial components and other materials. Financial enterprises or meteorological systems or companies that constantly collect, collate and use massive amounts of data on a regular basis are also ideal projects for ELT processes.
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