Are you new to the world of data architecture?
Boggled by new terms like ELT vs ETL, Data Warehouse, Data Lineage, Data Mesh, and Lakehouse?
Let me help you step into the world of new tech and how it helps with data management, and explain a bit more about ELT – why you are hearing about it a lot these days.
Introduction
Let me begin with Forrester Research, which says that the ELT market is expected to grow to $3.4 billion by 2027.
This growth is driven by the increasing adoption of cloud computing and the growing demand for real-time analytics. You can read the report here: “The Forrester Wave™: Enterprise Data Warehouse Platforms, Q1 2023,” March 2023.
This might be locked behind a paywall, so let me save you some bucks and share this study’s key highlights or takeaways.
The Forrester Research
1. The cloud data warehouse market is growing rapidly, driven by the increasing adoption of ELT and the need for real-time analytics.
2. The major players in the cloud data warehouse market are AWS, Google Cloud Platform, and Microsoft Azure. All three vendors offer cloud data warehouse solutions that are well-suited for ELT.
3. ELT is a data integration approach well-suited for cloud data warehouses.
This research says that ELT is a key trend in the cloud data warehouse market. It is a powerful and flexible data integration approach that can help organizations streamline their data integration processes, improve data quality, accelerate time to insights, and reduce a lot of overhead costs.
Markets and Markets Research
While I was researching more on the ELT, I came upon another research that talks about the future of ELT.
This is a forecast report by Markets and Markets – Extract, Load, and Transform (ELT) Market by Component (Solution and Services), Deployment Type (On-Premises and Cloud), Organization Size (SMEs and Large Enterprises), Vertical (BFSI, Retail, Healthcare, IT & Telecom, and Other), and Region – Global Forecast to 2027.
This huge report talks a lot about the ELT market, deployment type, organization adoptions, etc.
But here are the key elements that I have found:
1. The ELT industry is expected to grow in the coming years as more and more organizations adopt ELT to streamline their data integration processes.
2. ELT is particularly well-suited for organizations that must process large volumes of data in real-time, such as e-commerce companies, financial institutions, and media and entertainment companies.
So, now that you know what the market forecast about the ELT is, let’s dive into the basics:
What is ELT?
It stands for Extract, Load, Transform—a paradigm that flips the traditional ETL (Extract, Transform, Load) process on its head.
The raw data is first loaded into a destination, typically a data warehouse, and then transformed within that destination.
How is this a game-changer?
Think about your data movements. Instead of shaping your data before it reaches its destination, ELT harnesses the power of modern data warehouses to perform transformations in situ.
In situ here means “that the data is transformed in the same place where it is stored.”
This strategic shift is a game changer – let me explain why
When you are transforming the data in ELT, it is already loaded in the data warehouse – which means you get the following:
1. Faster Analysis: You get insights from the data quickly, as there is no wait time involved here for data transformation.
2. Efficient Data Integration: There is no need to move data between different systems, so obviously, you improve your data integration process.
3. Cost-effectiveness: You don’t have to hold multiple servers and bear the additional cost of data integration by purchasing ETL tools.
When you transform data closer to the source, you get better transparency and latitude to apply your business rules, optimizing your project performance and retaining quality data.
Now that I have already touched on some interesting ELT advantage points above let me dive into details a bit here and explain the intricacies of ELT so that you get a clearer understanding.
The ELT Process
Coming straight to the point –
Data Extraction
It is the first step in the ELT process. All the data gets drawn from diverse sources, whether structured, unstructured, or even bad information. Here are some data extraction methods:
– Batch Extraction: In this method, data is collected in chunks or batches at scheduled intervals. Let me help you understand with an example.
Assume a retail business like Walmart that has multiple stores across the states. They might go with a batch extraction overnight to capture the sales data from their various stores across the country. This could be batch data in the form of daily analytical reports.
– Change Data Capture (CDC): It is a more dynamic approach – as it allows the identification and capture of changes in the source data since the last extraction.
This kind of extraction is suited for those businesses in the finance sector. So if a business wants to monitor stock prices in real-time and capture changes instantly to analyze market trends – they would prefer the CDC method as it will allow them to make timely investment decisions.
– Real-Time Extraction: It streams data continuously as it’s generated. This is the buzzword for today’s generation – REAL-TIME!
A good example of this would be a brand like Facebook which thrives on real-time data. It allows them to deliver instant updates, ensuring users are always in the loop.
– API Integration: This is more of a standardized way to connect and extract data from applications. If you know about data scrappers, you know how an API works.
A good example of this would be an e-commerce business with a payment gateway. API integration enables the seamless extraction of transaction data, ensuring a smooth and secure checkout process.
Data Loading
Once extracted, this gets loaded typically to a data warehouse or storage system.
What is a Data Warehouse?
It is a central data repository that is structured and optimized for querying and analysis.
Why do we load the data first?
Loading into a data warehouse consolidates disparate data sources into a unified structure.
Techniques for Efficient Loading
Here are key techniques to ensure a smooth and swift loading performance:
- Parallel Processing: It basically involves breaking down data into chunks and loading them simultaneously. This technique accelerates the loading process.
- Incremental Loading: Instead of reloading all data every time, incremental loading focuses on updating only the changes since the last load.
- Data Compression: Data compression reduces the storage footprint, enabling faster loading and minimizing the storage requirements of the data warehouse.
Here are some technologies that I believe are pioneering the loading act:
- Amazon Redshift: It is renowned for its speed and scalability. It efficiently loads large volumes of data into its data warehouse, making it a preferred choice for many productions. Check it out here: Redshift
- Snowflake: Its architecture allows for seamless and parallel loading, ensuring optimal performance. Learn more about it here: Snowflake
- Google BigQuery: It is known for its serverless data warehouse and effortlessly loads and analyzes data with minimal manual intervention. Check it out here: BigQuery.
Data Transformation
Transformation happens within the data warehouse.
What is In-Place Transformation?
Unlike traditional ETL, where transformation occurs before loading, in-place transformation unfolds within the data warehouse itself.
The major benefit of transformation happening in situ is that it can leverage the computational power of the data warehouse, allowing for dynamic, real-time adjustments.
Transformation Challenges and Solutions
| Challenges | Solutions |
| Data Quality Issues: Sometimes, data may not be perfect. Incomplete or inconsistent data can disrupt the transformation process. | Data Cleaning and Validation: You can implement robust data cleaning processes to address inconsistencies. |
| Performance Bottlenecks: Intensive transformations may lead to performance bottlenecks, slowing down the entire production. | Optimization Techniques: You can consider optimization techniques such as indexing, partitioning, and caching to enhance the performance of transformations. |
| Scalability: As the volume of data grows, scalability can become a concern for transformation processes. | Parallel Processing and Distributed Computing: Focus on dividing the transformation workload through parallel processing and leverage distributed computing to scale horizontally, and you will be able to accommodate increased data volumes. |
| Real-Time Adaptability: In dynamic environments, the ability to adapt and transform data in real time is crucial. | Streamlined Architectures: To resolve this, adopt streamlined architectures that support real-time data processing, allowing for instant adaptation to changing data landscapes. |
Now, Let me explain the differences between ELT and ETL in detail a bit more.
Key Differences Between ELT and ETL
Timing of the Transformation:
| ETL | ELT |
| Transformation occurs before data reaches the destination. | Transformation happens within the destination. |
Scalability and Performance:
| ETL | ELT |
| Challenges are often faced when dealing with large datasets due to the need for pre-transformation. | Due to the scalability power of modern data warehouses – Efficiently processing large volumes of data without pre-transformation limitations becomes easier. |
Flexibility and Real-Time Processing:
| ETL | ELT |
| You can only go with batch processing, making it less adaptive to real-time data changes. | You get the flexibility and real-time processing capabilities. Any changes in the data can be immediately accommodated with the ELT process. |
Importance of Data Integration
Now, why does data integration matter?
Well, imagine your business data as puzzle pieces scattered across various platforms. These puzzle pieces will remain disjointed without integration, and the bigger picture—the actionable insights—will stay elusive.
Data integration, streamlined by ELT, acts as a foundation that binds your disparate data sources.
It helps in creating a unified, readable record from the mess of raw information. This gets applied to any industry operational function – whether you’re dealing with customer records, sales transactions, or metrics.
“A seamless data integration is the key to unlocking the full potential of your data assets.”
Evolving ELT Technologies
These evolving technologies and tools play a crucial role in facilitating data transformation within the ELT framework
1. Apache Spark: It is a powerful open-source data processing engine that provides a unified analytics engine for large-scale data processing. It’s especially adept at in-memory data processing, making it well-suited for complex transformations.
Use Case: Spark’s DataFrame API and Spark SQL allow for expressive transformations, making it an excellent choice for in-place transformations within data warehouses.
2. Talend: It is an open-source data integration tool that supports ELT processes. It provides a visual design interface for building data integration workflows, making it easy to design and implement complex transformations.
Use Case: Talend can be used to design and execute transformations within data warehouses, offering a user-friendly environment for defining business logic and data manipulations.
3. AWS Glue: It is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load data for analysis. It can perform in-place transformations on data stored in Amazon S3 and other data sources.
Use Case: AWS Glue can automatically generate ETL code, making it efficient for transforming data at scale. It’s seamlessly integrated with other AWS services, providing a comprehensive solution for ELT processes.
4. Snowflake’s Data Sharing and Cloning: It is a cloud-based data warehousing platform that offers innovative features for data sharing and cloning. These features enable efficient transformation and sharing of data within the Snowflake environment.
Use Case: Snowflake’s data sharing allows organizations to share transformed data sets securely between different Snowflake accounts, promoting collaboration and accelerating analytics.
5. Databricks Delta: It is an optimized data lake technology that brings reliability and performance to big data analytics. It facilitates efficient transformations on large datasets stored in data lakes.
Use Case: Delta Lake, coupled with Apache Spark, allows for ACID transactions and schema enforcement during transformations, ensuring data quality and reliability in large-scale analytics projects.
Let me do a comparative analysis of some of the top evolving tools based on my personal experience:
Comparative Analysis of ELT Technologies
| Feature | Talend | Apache NiFi | Azure Data Factory |
| Ease of Use | User-friendly interface. | Visual design for data flows. | Native integration into Azure services. |
| Scalability | Scalable for both on-premises and cloud deployments. | Well-suited for handling real-time data at scale. | Designed for scalability in Azure cloud environments. |
| Connectors | Extensive library of pre-built connectors. | Offers a variety of processors for data ingestion and transformation. | Rich set of connectors for diverse data sources. |
| Integration | Supports on-premises and cloud deployments. | Integrates well with various data storage and processing systems. | Native integration into the Azure ecosystem. |
Here are some of my personal recommendations:
If Scalability and Cloud Integration are your top priorities –
I would recommend: Microsoft Azure Data Factory
Why?
It is seamlessly integrated into the Azure ecosystem and provides scalability and native support for cloud-based data integration.
If you are looking for a Comprehensive Open-Source Solution –
I would recommend: Talend
Why?
It is a very easy-to-use open-source platform. In fact, one of the first projects that I have worked on – introduced me to Talend. I just fell in love with its user-friendly interface and its extensive library of connectors. It makes it perfect for projects that require diverse data integration.
If you are looking for a solution that offers Real-Time Data Ingestion and Transformation –
I would recommend: Apache NiFi
Why?
Apache NiFi’s focus on data automation and real-time data flow management makes it an ideal choice for scenarios requiring immediate data processing.
Wrapping Up
I hope I was able to give as much information as possible to help you understand ELT and now let me just wrap up here with how businesses can prepare better and adopt new tech to facilitate seamless data integration.
1. Embrace Cloud-Native Solutions:
If you are a business owner, start focusing on investing in cloud-native ELT solutions to leverage the scalability, flexibility, and cost-efficiency of cloud platforms. The cloud provides an agile environment that can adapt to changing data processing requirements.
2. Keep Yourself Informed on Emerging Technologies:
There are tons of new things coming, and it is imperative to keep a keen eye on emerging technologies in the data integration space. Stay informed about advancements in ELT tools, machine learning, and artificial intelligence, as they can significantly enhance data processing capabilities.
3. Invest in Data Governance and Security:
With data becoming more valuable, you have to prioritize robust data governance and security measures. Ensure compliance with data protection regulations and implement practices that safeguard sensitive information throughout the ELT process.
4. Foster a Data-Driven Culture:
It is high time to cultivate a data-driven culture within your organization. Start encouraging collaboration between data engineers, analysts, and stakeholders to derive actionable insights from integrated data, fostering innovation and informed decision-making.
5. Future-Proof Your Data Architecture:
ELT is not just a trend; it’s a strategic approach to data integration that aligns with the evolving nature of business intelligence. By adopting ELT, you can future-proof your data architecture, ensuring it remains agile and responsive to changes.
ELT KEY TAKEAWAYS FOR YOU
- Efficiency: ELT streamlines data integration, optimizing processes for efficiency and scalability.
- Real-Time Insights: The in-place transformation enables real-time data processing, empowering businesses with timely insights.
- Scalability: Modern data warehouses and cloud-native solutions provide the scalability needed to handle growing volumes of data.
Closing Thoughts!
ELT is not just a technological evolution—it’s a paradigm shift that empowers businesses to navigate the complexities of the data landscape with grace.
Remember, the true power lies not just in the tools and technologies but in the strategic mindset of leveraging data as a valuable asset. So, embrace ELT, embrace the future, and lead your business to new heights.