The world is awash in data, a fact that is driving profound changes to the way companies engage with their customers, plan for the future and approach nearly every aspect of their operations. And the emergence of artificial intelligence and advanced analytics has only pushed this trend into overdrive, as neither of these technologies can work without being fed staggering volumes of data. The increasing data volumes necessitate efficient data processing techniques, which are crucial for managing large datasets and enabling faster insights
These changes have driven a corresponding shift in the kinds of tools needed to train, deploy and update cutting-edge AI and analytics models, just as the transition from rudimentary shelters to skyscrapers required more complex construction techniques and heavier machinery.
Traditionally, extract, transform, load (ETL) pipelines bore a lot of this load, but for certain applications, more flexible extract, load, transform (ELT) processes have been necessary — particularly in the era of cloud data warehouses.
As Preetam Kumar, Director of Product Marketing at Informatica, put it, “In recent years it’s become clear that truly scalable analytics and AI initiatives require robust ELT strategies, and these have to be underpinned by advanced data architectures.”
Each of the primary data architectures represents a distinct strategy for optimizing ELT workflows to support the burgeoning use of AI and analytics. It can be tricky to sort out which makes sense in a particular circumstance. This article aims to guide forward-looking businesses to help select the most effective data architecture for their purposes by showing how each strategy can best support AI and analytics.
What is ETL Strategy?
Now that we’ve established why ELT is essential in the AI era, let’s dive deeper into what it really means and how data transformation plays a crucial role in this process.
ETL vs ELT strategy?
First, it’s important to clarify what an ELT strategy is and, especially, how it differs from an ETL strategy.
The primary distinction is an obvious one — indicated by the order of the letters in the acronyms “ETL” and “ELT.”
With an ETL pipeline, data is initially extracted, then transformed, and only then does it land in the final database. The problem is that this second transformation step can slow the overall pipeline considerably: Each data point is being manipulated or altered, even if it’s not ultimately pertinent.
An ELT pipeline, by contrast, extracts the data and loads it directly into a data lake (a centralized repository that stores raw, unstructured data), such as Microsoft Azure, Snowflake or Databricks. Because the data arrives raw, ELT tends to be faster and more flexible than ETL; if and when a particular bit of information is needed, it can be transformed as appropriate.
An ELT strategy, then, is the basic approach an enterprise uses to gather its data, load it into the data lake, then decide how to transform data when it’s needed within the target system.
The Relevance of ELT to Modern AI and Analytics
AI and analytics processes are data-hungry. Depending on the specific result being sought, they can require massive amounts of structured data (the kind found in neat rows in a spreadsheet or dataframe) as well as unstructured data (from transcripts, emails and the like).
“As powerful as artificial intelligence has become,” says Kumar, “we can’t forget that it’s ineffective without high-quality data. Whether it’s well-ordered rows of customer purchase decisions or more free-wheeling product reviews, the more you can feed into an analytics engine, the better your results are likely to be.”
Data engineers are central to this process, streamlining data integration and establishing efficient connections to various data sources. Their work is particularly evident in ELT processes, which enable raw data ingestion before transformation, providing the agility needed to accelerate model training and retraining.
Take retail, for example: merchants routinely utilize ELT to quickly ingest customer data from multiple sources (purchase history, browsing behavior, social media engagement, etc.) and build customer segments for personalized recommendations, a feat that would be challenging without efficient data integration and streamlined data pipelines.
3 Enhanced ELT Strategies to Power AI and Analytics Success
Each data architecture offers unique benefits and challenges for your ELT strategy. In this section, we’ll explore them in more detail.
The big three are:
Data lakehouse architecture
Data fabric architecture
Data mesh architecture
Strategy 1: Data Lakehouse Architecture
As its name implies, the data lakehouse architecture aims to combine the strengths of data lakes (which store raw, untransformed data) and data warehouses (which contain data that has been pre-structured for use in analysis).
Data lakehouses are often built according to the tripartite “medallion” architecture:
In the bronze layer, raw data is ingested.
In the silver layer, data is cleaned and refined.
In the gold layer, data is ready for consumption.
The benefit of a data lakehouse architecture is that it substantially streamlines data organization. It’s clear what each layer is for and exactly what needs to occur as data works its way through the process.
Moreover, a data lakehouse supports data scientists in model building because it allows them to do so directly from raw or semi-processed data (when appropriate). Overall, this leads to improved storage and compute efficiency in AI model development, which can be a significant competitive advantage.
Strategy 2: Data Fabric Architecture
A data fabric is an architecture that integrates data across both on-premises and cloud environments to create a unified view.
“Anyone with experience in data wrangling or data-heavy analytics work knows how much time can be lost because of silos,” says Kumar. “There are often subtle little sources of friction involved in feeding models data from multiple sources, which is why data fabrics have been such an exciting development among data enthusiasts.”
Two core features of a data fabric architecture are automated data discovery and data cataloging as well as real-time data access from disparate sources. Given the definition of a data fabric presented above, this makes sense: with a unified view of data across different data silos, finding and organizing data is much easier, as is utilizing the data when it’s needed.
This is particularly evident in data warehouse environments, where data is loaded and transformed to handle large volumes of raw data and enable efficient real-time access. These architectural features have the dual benefit of eliminating troublesome data silos while also simplifying data governance, the latter of which is a growing concern in a world fraught with data breaches and invasions of privacy.
One use case for which a data fabric architecture shines is powering real-time insights from distributed data sources. While it’s still more common to use a small number of larger data stores, applications like edge computing require gathering data quickly from dozens or hundreds of different places (e.g., telemetry from drones or automated vehicles) and leveraging it for on-the-fly decision-making. In these cases, success depends on being able to quickly pull and use this data for real-time decision-making.
Strategy 3: Data Mesh Architecture
The distinguishing feature of a data mesh is that it decentralizes data ownership by giving responsibility to individual business units, such as sales or marketing. This approach makes it easier to scale data management across an organization. The responsibility of managing types of data — for example, sales data — is assigned to the appropriate team, rather than a single centralized “data management” department that could slow things down. For this same reason, a data mesh architecture encourages collaboration and faster decision-making.
How to Choose the Right Architecture
With all this groundwork laid, the final remaining task is to decide which data architecture makes the most sense for a given set of constraints.
The major factors to consider are:
The type of data being used (with the two big variants being structured and unstructured)
Which business use cases need to be supported (i.e., is it more important to train AI models or utilize real-time analytics?)
The kinds of resources an organization can leverage
Take these factors into account as you weigh the advantages of the architectures discussed above. This approach will help you be better positioned to determine whether a data mesh, data fabric or data lakehouse is the most appropriate architecture for your needs.
In general, each architecture aligns with specific organizational needs:
Mesh: Ideal for decentralized data and flexible access.
Fabric: Best for centralized data with integrated governance and analytics.
Lakehouse: Suitable for handling large volumes of both structured and unstructured data in a cost-effective way.
ELT: The Key to Scaling AI and Analytics
AI and analytics are rapidly becoming technologies that will play a pivotal role in business, politics, education and many other domains. The key to using them successfully is clean, accessible, well-structured data. Robust ELT strategies underwritten by lakehouse, data fabric and data mesh architectures are essential for refining raw, messy, scattered data into high-octane fuel that drives innovation and growth.
There are several considerations when evaluating ELT solutions, but two stand out as particularly important for selecting a platform: 1) maximized automation in the ELT process to ensure optimal ROI on human labor, and 2) compatibility with each of the three architectures above.
The Informatica Intelligent Data Management Cloud (IDMC) fits these criteria. Leveraging AI to automate as much of the ELT workflow as possible, and uniquely offering unmatched breadth and depth of connectivity to support all three primary data architectures, IDMC simplifies the processes for organizations. IDMC enables them to catalog, integrate, govern, secure and share data — and thrive as a result.
To explore this topic further, download a copy of “Modern Architecture for Dummies.”