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Few data concepts are more polarizing than ETL (extract-transform-load), the data preparation technique that has dominated enterprise operations for several decades. Developed in the 1970s, ETL shined during an era of large-scale data warehouses and repositories. Enterprise data teams centralized data, layered reporting systems, and data science models in addition, and enabled self-service access to business intelligence (BI) tools. However, ETL has shown its age in an era of cloud services, data models, and digital processes.
Searches like “Is ETL still relevant/in-demand/obsolete/dead?” fill results on Google. The reason is that enterprise data teams are groaning under the weight of preparing data for widespread use across employee roles and business functions. ETL does not scale easily to handle large volumes of historical data stored in the cloud. It also does not provide the real-time data needed for rapid executive decision-making. Additionally, creating custom APIs to provide data to applications creates significant management complexity. It's not uncommon for modern businesses to have 500 to 1,000 channels in place as they look to transform data and equip users with self-service access to BI tools. However, these APIs are in a constant state of evolution, as they must be reprogrammed when the data they extract changes. It is clear that this process is too fragile for many modern data requirements, such as edge use cases.
Additionally, application capabilities have evolved. Source systems provide business logic and tools to enforce data quality, while consumer applications enable data transformation and provide a strong semantic layer. Therefore, teams have less incentive to create point-to-point interfaces to move data at scale, transform it, and load it into the data warehouse.
Two innovative techniques point the way to enabling data democratization while minimizing transformation burdens. Zero ETL makes data available without moving it, while reverse ETL pushes, rather than pulls, data to the applications that need it as soon as it is available.
Zero ETL optimizes the movement of smaller data sets. With data replication, data is moved to the cloud in its current state for use in queries or data experiments.
But what if computers don't want to transfer data at all?
Data virtualization abstracts servers from end users. When users query data from a single source, that output is returned to them. And with query federation, users can query multiple data sources. The tool combines results and presents integrated data results to the user.
These techniques are called zero ETL because there is no need to create a pipeline or transform data. Users manage data quality and aggregation needs on the fly.
Zero ETL is ideal for short-term ad hoc data analysis, as running large queries on historical data can hurt operational performance and increase data storage costs. For example, many consumer packaged goods and retail executives use zero ETL to query daily transactional data to focus marketing and sales strategies during times of peak demand, such as holidays.
Google Cortex provides accelerators, allowing zero ETL in SAP Enterprise Resource Planning system data. Other companies, such as one of the world's largest retailers and a global food and beverage company, have also adopted zero ETL processes.
Zero ETL Profits Include:
- Providing access speed: Using zero ETL processes to provision data for self-service queries saves 40% to 50% of the time it takes to use traditional ETL processes because you don't need to create pipelines.
- Reduce data storage requirements: Data does not move with data virtualization or query federation. Users only store query results, reducing storage requirements.
- Offering cost savings: Teams using zero-ETL processes save 30% to 40% on data preparation and storage costs compared to traditional ETL.
- Improved data performance: Because users see only the data they want, results are delivered 25% faster.
To get started with zero ETL, teams should evaluate which use cases are best suited for this technique and identify the data elements they need to execute it. They should also configure their zero ETL tool to point to the desired data sources. Teams then extract data, create data assets, and expose them to downstream users.
Reverse ETL techniques simplify data flows to downstream applications. Instead of using REST APIs or endpoints and writing scripts to extract data, teams leverage reverse ETL tools to push data into business processes on time and in full.
Using reverse ETL provides the following benefits:
- Reduce time and effort: Using reverse ETL for key use cases reduces the time and effort to access data for key use cases by 20-25%. A leading cruise line leverages reverse ETL for digital marketing initiatives.
- Improve data availability: Teams have greater certainty that they will have access to the data they need for key initiatives because 90% to 95% of target data is delivered on time.
- Decreasing costs: Reverse ETL processes reduce the need for APIs, which require specialized programming skills and increase management complexity. As a result, teams reduce data costs by 20-25%.
To get started with reverse ETL, data teams should evaluate use cases that require on-demand data. They then determine the frequency and volume of data to be delivered and choose the appropriate tools to handle these data volumes. They then route the data assets from the data warehouse to their target consuming systems. Teams must prototype with a load of data to measure efficiency and scale processes.
Zero ETL and reverse ETL tools give teams new options for delivering data to users and applications. They can analyze factors such as use case requirements, data volumes, delivery times, and cost factors to select the best option for delivering data, whether traditional ETL, zero ETL, or reverse ETL.
Partners support these efforts by providing information on the best techniques and tools to meet functional and non-functional requirements, providing a weighted scorecard, performing a proof of value (POV) with the winning tool, and then operationalizing the tool for more cases. of use.
With zero ETL and reverse ETL, data teams achieve their goals of providing users and applications with the data they need where and when they need it, driving cost and performance gains and avoiding transformation headaches.
Sen Rabbitis an experienced professional with a career spanning over 16 years in the technology and decision science industry. He currently serves as VP of Data Engineering at Tredence, a leading data analytics company, where he helps organizations design their ai-ML/Cloud/Big-data strategies. With his expertise in data monetization, Arnab uncovers the latent potential of data to drive business transformations among B2B and B2C customers across diverse industries. Arnab's passion for team building and his ability to scale people, processes and skill sets have helped him successfully manage multi-million dollar portfolios across various verticals including Telecom, Retail and BFSI. He previously held positions at Mu Sigma and IGate, where he played a crucial role in solving customer problems by developing innovative solutions. Arnab's exceptional leadership skills and deep knowledge of the field have earned him a position on the Forbes tech Council.
