ETL vs ELT
By Sami Haddad
June 3, 2024

When it comes to data integration, customers are almost always looking to answer fundamental analytic questions about their business:

  • How effective is my sales team?
  • How are my social media channels performing?
  • Which business unit or team is doing everything right?

To answer questions to this granularity, not only do you need access to your data in an analytical format, you need a data warehouse to centralize your data from all your different data sources.

Begin by E-xtracting

Only then can you get the 360-degree insight you need to understand your business.

Precog supports over 2000 application (API) sources through our AI platform, which automatically generates analytics-ready tables for the data warehouse or database of your choice. We do so using the Extract Load and Transform (or ELT) framework — let’s discuss why this is advantageous below.

In the beginning: ETL

In the early days of data integration, databases were not designed to hold large amounts of data as customers were always having to choose storage (how much data can my database hold?)
vs. performance (how quickly can I sift through my data and model such?).

Due to this tradeoff, customers often focused on the performance of their databases by limiting their data consumption. That’s why the data integration market, at this point, was focused on ETL (Extract, Transform, and Load).

Data integration companies bet their business on data vendors’ inability to separate compute and storage.

Customers always needed to pick and choose which columns or rows they wanted to transfer. This meant most databases had snippets of data rather than everything the system offered, making it virtually impossible for any business to analyze every angle of its decision-making.

Only in the last 15 years has the database market experienced marked innovation with the advent of online data warehousing. The Google Big Query (GBQ) announcement in mid-2010 was followed by Snowflake and Amazon Redshift — this became the moment data warehouses went to virtually unlimited storage and performance.

How Data Warehouses Enabled ELT

At this point, ETL was no longer a desirable model – companies were no longer constrained to choosing which data they wanted, so why would they? The answer to that question became Extract, Load, and Transform (ELT); customers asked for data first and asked questions later using analytics-ready tables in their destination.

Outside of convenience, there are many immeasurable benefits ELT brings to the market, including:

Data Integrity. By moving the data first and transforming it later, ELT enables organizations to store vast amounts of analytics-ready data, preserving its integrity for future analysis. When your organization begins transforming data prior to loading it into the data warehouse, you no longer have control on the context and run the risk of low-quality or inaccurate analysis.

Scalability. As data volumes continue to soar, ELT architectures can easily accommodate this growth without necessitating significant changes to the underlying infrastructure. This scalability empowers businesses to adapt to evolving data needs and leverage insights from increasingly diverse data sources.

Agility. ELT promotes an iterative approach to data analytics. By loading analytics-ready data directly into the target system, analysts gain immediate access to a comprehensive dataset, eliminating the need to wait for extensive transformation processes to complete and to preserve precious API calls.

Consistency. ELT minimizes data movement, reducing the risk of errors throughout the analytics pipeline. With less data manipulation during the extraction and loading phases, organizations can streamline their workflows and maintain the fidelity of their datasets, thereby enhancing the reliability of their analytical insights.

ELT Evolved from ETL to Become the Standard

While ETL allowed customers to maximize limited database performance by filtering data, the marked improvements in the data warehousing space led to a new approach to data integration, ELT.

At Precog, we combine the philosophy of ELT with our AI-backed connector generator to deliver a seamless data integration experience from large enterprises to small and medium-sized businesses worldwide.

NEWS & BLOG

Ready to Start?

From Our Customers

Localize

We chose to use Precog because they were the only company willing to handle our complex data connections. Precog was extremely helpful getting us set up and running smoothly. Since then it has been one of those tools that just works solidly and reliably which is one less thing our team nee... Read More

Derek Binkley - Engineering Manager
Cured

Precog is an important partner for Cured and a critical member of our data stack. The Precog platform has delivered data connectors to necessary data sources other vendors could not or would not, and in a very short timeframe. The product is intuitive, efficient, cost-effective, and doesn&... Read More

Ashmer Aslam - CEO Cured
Walnut St. Labs

Precog lets us prototype analytics projects quickly — building marketing dashboards based on data from a variety of sources — without needing a data engineer or developer — we create new data sources in a few hours to sources like Brightlocal, a popular local SEO SaaS solution, and h... Read More

Chris Dima - CEO
Alteryx

We welcome Precog to the Alteryx technology partner ecosystem as a partner extending the capabilities of our platform, further simplifying analytics for our customers.

Hakan Soderbom - Director of Technology Alliances
SouthEnd

We recognized a need in our customer base to perform advanced analytics on SAP data sets — we performed an extensive evaluation of Precog and chose it as a strategic solution for our go to market needs based on its performance and given their strong strategic relationship with SAP.

Alfredo Poncio - CEO
SouthEnd
SendaRide

Precog is the vital tool in our ability to pull data from a variety of business sources quickly and cleanly. Our internal MongoDB backend, as well as other cloud services like Hubspot, were a constant challenge to the business teams desire for reporting data prior to using Precog. With the... Read More

Josh Wilsie - VP