Ultimate Guide to ETL Tools for Modern Business Intelligence

Introduction

Extract. Transform. Load. 3 words that sound so simple, but when we consider modern technology platforms, AI and all the exciting ways data can be transformed – we realise there are quite a few considerations around the best ways to do it.

Firstly, here’s a basic definition of ETL Tools:

Extract: 

• ETL tools extract raw data from various sources such as databases, APIs, files, or web services, ensuring it’s available for further processing. 

Transform: 

• ETL tools transform the extracted data into a consistent and clean format, ready for analysis or storage. This involves data cleansing, enrichment, aggregation, and applying business rules. 

Load: 

• ETL tools load the transformed data into a target system/platform or database, ensuring it’s accessible for reporting, analytics, and decision-making. Common destinations include data warehouses, data lakes, or other storage solutions. 

Business data is typically ‘all over the place’, in different platforms, software and spreadsheets, and we’re seeing that a key differentiator for industry leaders lies in their ability to track, consolidate and augment their data to drive more profitable decision making. The importance of ETL tools when it comes to business intelligence is not to be underestimated in this case.

This blog aims to clarify what ETL has meant traditionally, and what modern ETL tools can do for operational efficiency and tech-ROI right now. We’ll also go over the key considerations to help you opt for a top set of tools, and importantly – the right partner. Because there are so many options out there – it’s critical to understand this topic fundamentally to see through the sales-speak and make better decisions for your unique business case. 

Index: 

• Differences between traditional and modern ETL tools 
• Key considerations to choose the right ETL Tools, platform and partner
• Modern ETL Tools in Action
• Conclusion 

Differences between traditional and modern ETL tool

Processing Method: 

• Traditional ETL tools are based on batch data processing, where data is collected and processed in predefined blocks. 
• Modern ETL tools support real-time data streaming and processing (or high-speed batch data processing), allowing businesses to access and use data almost immediately. 

Flexibility: 

• Traditional ETL systems are tailored primarily to relational databases and are less equipped to handle unstructured data. 
• Modern ETL tools offer greater flexibility, capable of handling a variety of data types, including both structured and unstructured data. 
• The best ETL tools provide both built-in connectors and transformations, as well as bespoke transformations. Choose based on your company’s specific requirements and the technical expertise of your IT team and potential partners. 

Infrastructure: 

• Traditional ETL required on-premises databases and data pipelines – subject to poor performance and uptime SLAs. 
• Modern ETL tools are cloud-based, making them fast, easy to maintain and infinitely scalable… 

Scalability: 

• Using traditional ETL technology makes it tough to scale effectively. As data volumes and complexity increase, often data utility and granularity are sacrificed for performance. Bottlenecks and outages are far more common in outdated ‘frankenstein’ ETL frameworks – limiting effective scalability.
• Modern ETL does not have the speed limitations of traditional tools and can keep pace regardless of the size of the data load. 
• Parallel processing (as opposed to sequential processing) distributes data processing tasks more efficiently across multiple nodes or clusters. This allows for the handling of large volumes with much better performance. 
• Many modern ETL tools are built for the cloud, leveraging services like Google Cloud or Microsoft Azure – meaning cloud-based ETL tools can automatically scale up or down based on demand.
• Cloud providers also offer managed ETL services, simplifying infrastructure management, allowing users to focus on data transformations and avoid manual server setup and maintenance. 

Management and User Interface Complexity/Simplicity 

• Traditional ETL tools most often require highly specialised staff managing complex interfaces. This means that:
  • It takes longer to solve problems.
  • There’s risk in terms of losing hard-to-replace skills within the company.
  • Critically, older ETL tools miss the boat in terms of enabling non-developers like business leaders and operations strategists to get the business intelligence they need. It becomes easy to aggregate data and generate the visualisations needed to make great decisions – fast.
• Modern ETL tools typically use user-friendly, drag-and-drop interfaces and visual workflows to get things done – which means it’s not only a lot faster to get the insights you need, but also far more accurate to begin with. Human error is much less of a factor when using sophisticated tools.

Security: 

• Traditional ETL systems typically require companies to manage their security systems using internal resources and networks. 
• In contrast, modern ETL offers more dynamic security options, often through Software as a Service (SaaS) models, allowing providers to manage security in the cloud. 
• Look out for ETL tools and platforms which tend away from using API keys, and more so use authorisation tokens to provide access to users and authorised 3rd parties. 
• More information on Matatika’s security documentation 

Cost and Maintenance: 

• Traditional ETL can be inflexible, expensive, and slow, which may not suit the agile, data-driven modern organisation. 
• Connecting dinosaur code to modern features as a shortcut to improving transformation of data within old ETL frameworks also generally comes with significant efficiency and maintenance costs. 
• Modern ETL tools are designed for the cloud and big data era, providing cost-effective solutions that are far easier to maintain and support. 
• At Matatika, we support the whole solution we deliver… 

AI Integration 

• Traditional ETL tools typically aren’t AI-enabled, meaning they don’t easily integrate with Large Language Models (LLMs) and other AI tech like: 
• Machine learning and predictive analytics to help companies anticipate trends and optimise processes. 
• Computer vision (CV) algorithms for tasks like object detection, facial recognition, quality control in manufacturing and analysis of shopper behaviour and store layouts in the retail sector. 
• Graph analytics to analyse relationships between entities (nodes) in a network – useful for fraud detection, social network analysis, and supply chain optimisation. 
• Time series analysis – a statistical technique that deals with time series data, or trend analysis. For example, stock market data, sensor data, or sales data.
• Modern ETL tools integrate with all of the above, and your best options will have been built from the ground up with AI in mind.  
• You’ll find this 5 minute demo video really interesting if you’d like to see a practical showcase of how an LLM connected directly and securely to both your data and the internet can enable far more intelligent insights and majorly improve speed and efficiency.

Key considerations to choose the right ETL Tools, platform and partner

Data Integration Needs 

• ETL tools should connect to a variety of data sources and destinations, so it’s important to opt for tools that offer a wide range of integrations and seamless movement of data between systems. You also need to ensure that connectors are consistently updated to keep up with upstream API changes. 
• This is probably not as tricky as it may sound. For example, you probably see your technology appear here right? 
• Matatika provides the complete set of ETL tools to rapidly load data from 500+ sources into your data warehouse. 

Accessibility and performance

• Arguably the most important question/consideration here is: “Will all your data be in one modern infrastructure?” It’s critical because it means your data will be transformable within in a fast, modern framework, and  accessible for non-developers (in terms of discovering, discussing and understanding the data).
  • Matatika provides data teams and business users all the important information about their data – where it comes from, where it is used, when it was last loaded, and how consistent it is. Sharing and searching trusted datasets becomes simple.
  • Using the Matatika Lab, data teams can configure new sources without writing any code. Even managing 100’s of customer centric connections becomes a breeze using standardised and trusted tools such as VS Code. 

Reliability

• Creating reliable data load pipelines also has a lot to do with ensuring you have complete version control release and rollback features. It’s really important to be able to deploy and promote changes between dev, staging, and production environments. With Matatika, git-enabled version control enables collaboration and rollback. 
  • Being able to develop and test every model prior to production release, and collaborate on dynamically generated documentation. Using Matatika you’ll be able to collaboratively deploy modular, portable, and documented analytics code. This enables your teams to deliver trusted, high quality datasets in even the most complex environments, and without a plethora of ETL tools. 

How strategic your partner is

Last but definitely not least – you need to consider the importance of choosing a partner that not just knows how to get things done on a technical level, but who can also see the wood for the trees.

Understanding strategic, commercial, operational and process-related opportunities is a critical first step towards a top technology solution, and your best route to value. In other words, your partner needs to bring ideas to the table about how best to do something as opposed to just saying ‘yes, we can do that’ to a possibly ill-considered set of instructions.

This, unfortunately is something you’ll need to feel-out with your potential partners. The best advice we can give you is to just keep this in mind and expect interesting insights when it comes to meetings. Also – do some research by reading some of their blogs, and most importantly – case studies.

At Matatika, we’re confident in this being one of our key differentiators. 

Modern ETL Tools in Action 

City Building and Engineering Services (CBES) increased revenue through customer service intelligence

Delivered by the Matatika platform leveraging top ETL tools, CBES turned service information on their facilities (air-conditioning, fridges and self-service machines) into externally visible business intelligence.

Information on maintenance and repair was taking too long to arrive due to manual processes. The information was also out of date, and impossible to quantify.

In terms of matatika’s impact, one report alone saved five hours per week of work-time…

Discover more about how Matatika connected static data from all around the organisation into live workspaces – delivering major cost and time efficiencies and a 20% increase in turnover year on year here.

CitySprint (same day logistics) were drowning in technology and versions of the truth

Delivering more than 50K parcels per year as a long-established leader in logistics, CitySprint generate a lot of data and lacked a single version of the truth across finance, sales and operations. So although data had become one of the company’s key strategic differentiators – the data technology itself had become obsolete or inadequate. 

Strategic objectives were slipping and taking far longer than anticipated due to the legacy complexity of their data technology and ETL tools. A lot had to change to ensure growth targets could be met.

CitySprint had:

• 3 ETL tools – Matillion, SQL Server Integration Services, and QLik 
  • Reduced to 1 more powerful platform (Matatika)
• 2 BI platforms 
  • Consolidated on to Power BI
• Limited testing and change management.  No code tools can be quick, but the complexity builds up very quickly in data
• 100s of models 
  • Reduced to 10s of tested and trusted datasets
• Frequent outages and incorrect data delivered to the business
• A data team that spent most of their time fire fighting

The result:

On the technology side, Matatika has enabled CitySprint to migrate all its internal business intelligence to Azure with a scalable, supported, and future proof platform. Day to day data issues have been dramatically reduced, strategic initiatives are being delivered faster and with fewer errors. “Freeing up the team to add real business value has been a revelation.”

With a trusted, single version of the truth, CitySprint now leverage data in more ways than ever, and are even beginning to explore powerful AI use cases enabled by the Matatika platform.

Read more about the CitySprint case study here.

Conclusion 

In summary, modern ETL tools are designed to be more adaptable, efficient, and scalable, meeting the needs of today’s fast-paced, data-driven businesses. They provide the ability to process data in real-time, support a wider range of data types, and offer cloud-based solutions that can reduce costs and increase agility. The right ETL solution will depend on your organisation’s unique context, data sources, and goals, but the key factors to consider are

• Data integration needs 
• Connectors needed 
• Customisability  
• Accessibility and performance 
• Reliability 
• How strategic your partner is

It’s become clear that simplifying complexity and gaining the strategic insight you need to make the best decisions about your data involves investing time into finding the right partner. 

The best way to figure out if we at Matatika are a good fit for your ambitions is to meet so we can hear more about your situation and requirements. We’re keen to have a dynamic conversation or demo about how we can get it done right.

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