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SAP Sapphire Madrid 2026: From AI Assistance to the Autonomous Enterprise

Posted on June 8, 2026June 8, 2026

A Few Days in Madrid

I had the opportunity to attend SAP Sapphire Madrid 2026 from 19 to 21 May, joining customers, partners, and SAP experts from across Europe to discuss the future of enterprise technology. While there were many interesting sessions throughout the event, the Global Keynote on 20 May, titled The Beginning of Better, stood out as the clearest expression of where SAP sees business technology heading next.

Presented by Christian Klein, Sebastian Steinhaeuser, Philipp Herzig, and Muhammad Alam, the keynote focused on a vision that goes beyond using AI as a productivity tool. Instead, SAP introduced its concept of the Autonomous Enterprise, where people define objectives, policies, and boundaries, while AI agents execute business processes within governed and trusted environments.

A Shift in How Enterprise AI Is Framed

One of my main observations from the keynote was that SAP is moving the conversation beyond AI assistants that simply help employees perform tasks faster. The focus is increasingly on AI systems that can carry out work autonomously while remaining under human oversight.

The centrepiece of this vision is the newly announced SAP Autonomous Suite. SAP described it as a framework that brings together AI agents, assistants, business applications, and governance capabilities into a single operating model. Rather than deploying isolated AI solutions, organizations would be able to orchestrate business processes that span multiple functions while maintaining visibility, compliance, and control.

This felt like an important distinction. Many organizations are currently experimenting with AI pilots and productivity tools, but the challenge often lies in connecting those capabilities to real business processes. SAP’s message was that enterprise AI needs to be embedded directly into operational systems rather than existing alongside them.

The Evolution of Joule

Another significant announcement was the evolution of Joule. Joule is now expanding into a broader AI workspace.

SAP presented Joule Work, Joule Assistants, Joule Agents, and Joule Studio 2.0 as components of a larger ecosystem designed to support both employees and autonomous business operations. The direction suggests a future where users interact with AI not only through conversational assistance, but also through specialized agents capable of executing defined business tasks.

What I found particularly interesting was how SAP positioned these capabilities as part of a coordinated environment rather than separate products. The emphasis was on enabling collaboration between people, AI assistants, and autonomous agents within existing business workflows.

Trust, Data, and Governance Take Center Stage

Perhaps the most important theme throughout Sapphire was trust.

SAP repeatedly emphasized that autonomous AI can only be effective when it is grounded in trusted business data and governed appropriately. This is where SAP Business Data Cloud, the new SAP Business AI Platform, and the SAP AI Agent Hub fit into the broader strategy.

The SAP Business AI Platform was presented as the foundation for building, governing, deploying, and operating enterprise AI agents. At the same time, SAP Business Data Cloud provides the business context that agents need to make informed decisions, while governance capabilities ensure transparency and accountability.

I was also interested to see SAP highlight its collaboration with Mistral AI and its commitment to trusted European AI and sovereign AI capabilities. For many organizations, particularly in Europe, questions around data sovereignty, regulatory compliance, and trusted AI infrastructure are becoming just as important as model performance.

Why This Matters

For organizations exploring AI adoption, the announcements at Sapphire were less about individual features and more about architecture and operating models.

Many companies have already discovered that deploying AI tools is relatively easy compared to scaling them across critical business processes. The challenge is creating an environment where AI can operate reliably, securely, and in alignment with business objectives.

The message from the keynote The Beginning of Better was clear: the next phase of enterprise AI is not simply AI assisted work. It is AI executed business processes operating under human governance. SAP’s vision of the Autonomous Enterprise brings together Joule, AI agents, trusted business data, and the SAP Autonomous Suite to make that transition possible.

Looking Ahead

Leaving Madrid, my impression was that enterprise AI discussions are becoming more practical and more operational. The focus has shifted away from experimentation toward questions of governance, execution, trust, and measurable business outcomes.

Whether organizations are ready for fully autonomous business processes today is another question. However, the direction presented at SAP Sapphire 2026 suggests that enterprise software is steadily evolving toward environments where humans set the goals and AI increasingly handles the execution. It will be fascinating to see how quickly that vision becomes reality over the coming years.

-Juuso Maijala, CEO

Leveraging AI in data stream loading: Matillion Data Productivity Cloud

Posted on August 12, 2025October 28, 2025

AI has been on everyone’s lips in the IT industry for the past few years, and its development has been rapid. Application providers have also developed and incorporated more and more AI-enabled capabilities into their software. Matillion is no exception.

Matillion’s Data Productivity Cloud (DPC) includes several AI-based objects for use in developing loading pipelines. These include, among others, Copilot for natural language generation, AI Notes for automated documentation, and components for interacting with large language models (LLMs) such as OpenAI, Azure OpenAI, and Amazon Bedrock. In addition, Matillion DPC offers components for vector database operations (Pinecone, Snowflake) and for interacting with AI services such as Amazon Textract, Amazon Transcribe, and Azure Document Intelligence.

In this blog, we will look at some of the opportunities offered by the Matillion Data Productivity Cloud (DPC) from three perspectives:

Using AI in loading pipeline development with Matillion’s Maia AI assistant,
Using AI in loading pipeline documentation, and
Using Snowflake’s Cortex AI functions in a business use case.

Using AI in Loading Pipeline Development

In Matillion, loading pipelines have traditionally been built by dragging loading components onto a canvas, then connecting them and defining the desired processing rules. This has been a clear, intuitive, and easy way to build loading pipelines. Matillion’s AI assistant, Maia, takes this one step further.

Maia is especially useful in cases where:

You have only recently started using Matillion DPC or are otherwise inexperienced with ETL/ELT tools,
The development environment is unfamiliar, or
You want suggestions on how to solve a specific loading-related technical problem.

In the example below, Maia was instructed to create a loading pipeline that:

Loads customer and order data from the SNOWFLAKE_SAMPLE_DATA database,
Joins the table data, and
Creates a table and loads the data into the newly created table.

Matillion can retrieve the correct source database and the desired tables from the metadata as separate table-read components, join the tables in a join component using keys, and use a rewrite component to create and load the table.

Maia can be used for further development of the loading process or in individual loading components. In the example below, Maia is used to add metadata fields to the load and to perform calculations.

*Maia adds an Add Metadata calculation component to the loading pipeline, in which it includes the metadata fields and their functions.*

In the calculation component, the desired calculation is defined, and Maia creates the complete calculation formula. This is a useful feature, especially in cases where less commonly used functions are applied

Using AI in Loading Pipeline Documentation

Documentation of loading pipelines often receives little attention or is not done at all. This frequently slows down further development of the loads, the fixing of errors, or, more generally, the understanding of the pipeline’s logic. Matillion DPC enables the automatic creation of descriptions for loading pipelines and individual components using AI, quickly and with just a few mouse clicks.

In the example below, the Add Metadata calculation component contains added fields and calculations that we want to highlight. With Matillion, automatic description generation provides a clear picture of the operations performed within the object.

*AI-generated description of the Add Metadata component.*

By using Maia, it is possible to automatically create a complete description of the entire loading pipeline, including the loading steps and logic in detail. This feature enables effortless documentation of the entire pipeline and is highly beneficial when there is a need to understand the operation of a previously unfamiliar loading pipeline.

*Maia-generated summary of the loading pipeline.*

Snowflake Cortex AI Use Case

Matillion DPC also enables the use of Snowflake’s AI capabilities with ready-made objects that can be added as part of regular loading pipelines. This low-code / no-code approach makes AI accessible to a wider group of users and lowers the threshold for adopting AI capabilities, as no special skills are required.

Below is a simplified example where AI is used to create responses to customer feedback. The feedback has been received in three different languages (Finnish, Swedish, and English). The Matillion orchestration pipeline contains three different stages (transformation jobs), each using different Snowflake Cortex functions. The stages are:

REVIEWS_1_TRANSLATE – Translates all feedback into English using the CORTEX.TRANSLATE function.
REVIEWS_2_SENTIMENT – Determines the sentiment of the feedback (positive / negative) using the CORTEX.SENTIMENT function.
REVIEWS_3_REPLY – Generates responses to the feedback using the CORTEX.COMPLETE function.

*Loading pipeline for automating customer feedback responses.*

In the first stage, the Cortex Translate object is used to translate customer feedback, that is loaded into Snowflake and written in multiple languages, into English. In the Cortex Translate object, the column to be translated is specified, along with the source language (in this case, automatic detection) and the target language, which is English. After the translation, the columns are renamed, and the data is loaded into the database for the next stage.

*Loading pipeline for translating customer feedback into English.*

In the second stage, the Cortex Sentiment object is used to identify the sentiment of the customer feedback. In the object’s settings, the column to which the sentiment analysis is applied is selected. Matillion creates a new column for the value, with a scale ranging from -1 (negative) to +1 (positive). Finally, in the loading pipeline, the necessary columns are renamed, and the data is loaded into the database for the final stage.

*Loading pipeline for determining the sentiment of customer feedback.*

In the final loading pipeline, the processing performed in the previous stages is used to generate a response to the customer feedback. At the start of the pipeline, a Filter object is used to split positive and negative feedback into separate data streams, based on the sentiment analysis carried out in the previous stage. Both data streams are then directed to their own Cortex Completions objects. The following are defined for these objects:

The model to be used for generating the response.
A system prompt that provides context for creating the response.
A user prompt that specifies the actual response.
The input to be used for generating the response: in this case, the feedback.

After generating the response, the data streams are merged, transformed into a columnar format, the columns are renamed, and the data is loaded into the database, for example, to be used by customer service systems.

*Loading pipeline for generating customer feedback responses.*

Summary

Matillion’s AI capabilities are not just technical accelerators, they are collaborative tools that bring data engineers, analysts, and business stakeholders onto the same page. They enable natural language interaction, automatic documentation generation, and the integration of diverse perspectives directly into data pipelines. Matillion bridges the gap between business and data systems, enabling understanding of actions without the need for technical skills such as code literacy. This opens the door to more agile development, faster iteration cycles, and, ultimately, more data products that are truly aligned with business priorities.

The AI objects in Matillion DPC that can be used within loading flows speed up and simplify the adoption of these capabilities in organizations. The Snowflake Cortex objects are a good example of this. Matillion DPC is used to build and orchestrate the loading pipeline, while the actual data processing is performed in Snowflake. This eliminates data transfers, supports real-time execution, and ensures that security practices are followed consistently throughout.

-Asko Ovaska

SAP and Databricks partner up—What’s in it for You?

Posted on February 20, 2025October 22, 2025

Exploring the Future of Data Engineering with SAP Business Data Cloud

In today’s rapidly evolving digital landscape, businesses are constantly seeking innovative solutions to enhance their data management and analytics capabilities. On February 13, 2025, SAP launched the Business Data Cloud (BDC), a new Software-as-a-Service (SaaS) product designed to provide a unified platform for data and AI. According to SAP BDC is a comprehensive platform revolutionizing the way organizations handle data and artificial intelligence (AI) applications. In this blog post, I will delve into the key highlights of SAP Business Data Cloud and its collaboration with Databricks.

Introduction to SAP Business Data Cloud

SAP BDC combines several powerful components, including Datasphere, SAP Analytics Cloud (SAC), SAP BW, Databricks, and Joule (AI), to offer a comprehensive solution for data and AI needs. This integration offers AI capabilities, data management, and application support, making it ideal for businesses looking to fully utilize their data.

Key Features of SAP Business Data Cloud

The SAP Business Data Cloud is built to address a wide range of data and AI requirements. Some of its key features include:

Comprehensive Data and AI Platform: BDC integrates various SAP and third-party data sources, providing a seamless flow from raw data to insightful analytics and AI applications.

Insight Apps: These ready-made SaaS products offer out-of-the-box solutions for data and AI needs, enabling businesses to quickly deploy and benefit from advanced analytics.

Custom Build Scenarios: BDC supports custom solutions, allowing organizations to combine SAP and third-party data to create tailored analytics and AI applications. It is also possible to copy Insight Apps components and to enhance copied functionalities with custom development.

The Role of SAP Databricks

A key feature of BDC is its integration with SAP Databricks. This collaboration brings Databricks’ powerful AI and machine learning (ML) functionalities to the SAP ecosystem, enabling businesses to leverage advanced analytics and AI capabilities within a single platform.

Benefits and Considerations for SAP BDC

The SAP Business Data Cloud offers several advantages that make it a compelling choice for businesses:

Single SaaS Platform for Analytics, AI, and ML: BDC provides a unified platform that integrates various SAP and third-party data sources, enabling seamless data management and advanced analytics.

SAP Databricks AI/ML Functionalities: The integration with Databricks brings powerful AI and machine learning capabilities to the SAP ecosystem, enhancing the platform’s analytical capabilities.

Insights Apps: BDC includes ready-made SaaS products that offer out-of-the-box solutions for data and AI use cases, allowing businesses to quickly deploy and benefit from advanced analytics.

Tight Integration to Business Processes: BDC is designed to integrate seamlessly with existing business processes, ensuring that data analysis and AI applications are closely aligned with SAP business processes.

While the SAP Business Data Cloud offers numerous benefits, there are a few considerations to keep in mind:

A New Product – What is the maturity?: As a new product, businesses should evaluate the maturity of BDC for their respective use cases and consider any potential challenges during the implementation phase.

SAP Joule dependency – how to integrate into your overall architecture? Joule is yet another co-pilot AI interface to your stack. You have to make sure that for each use case there is well thought through user experience either through Joule or some other co-pilot integrating with Joule that is in line with your overall architecture e.g. MS co-pilot.

All in SAP or Use Both SAP BDC and Other Non-SAP Tools?: Organizations should still consider whether it makes sense to fully commit to the SAP ecosystem or to use a combination of SAP BDC and other non-SAP tools to meet their data and AI needs.

What to expect?

The SAP Business Data Cloud represents a significant leap forward in the realm of data engineering and AI. By combining the strengths of SAP’s data management tools with Databricks’ AI/ML capabilities, BDC offers a platform for businesses to enhance their data analytics and AI applications. As organizations continue to navigate the complexities of the digital age, solutions like BDC will play a crucial role in driving innovation and success.

–Juuso Maijala, CEO & Founder

At Etlia Data Engineering we have a unique combination of expertise in both SAP and Databricks to support your business AI transformation. Want to know more? Book a meeting with us and let’s talk about how we can help your business to leverage SAP Business Data Cloud with Databricks!

Testing MS Fabric Review on ”Auto-create report” -feature

Posted on July 3, 2023October 21, 2025

One of our experts had previously produced a meaningful report of Finland’s Corona data. Now, after the launch of Microsoft’s new SaaS offering called Fabric, we will test its reporting feature which is supposed to ease the work of Data analysts and BI developers. With the “Auto-create report” -feature embedded to MS Fabric you can create insights from datasets with just one click. In the following text, we are going to compare the reports built by Fabric and our expert, and review if the quality of auto-created report matches the one produced by an expert.

Fabric’s auto-created report of Finland’s Corona data

How does it work?

From Fabric UI you can conveniently access your data. You are able to create datasets from the files and tables you have uploaded to OneLake, which is this new unified data source we discussed in the previous blog concerning MS Fabric. By selecting the dataset you would like to create the report of, you can decide whether you like to build the report from scratch or if you want the Fabric to automatically build the report.

When you decide to automatically create the report, Fabric picks up the columns from the tables it thinks are the most meaningful and creates the visuals to reflect the insights of that data. It creates a quick summary page to show the most important highlights on its opinion. It also writes a short text to summarize the insights of the visuals. You can then change the data you want to be projected and it automatically builds new visuals of the selected data.

Comparison

Using the “Auto-create report” -feature you can easily build a sufficient report which tells effectively the key insights of the data. You probably still need to do some work selecting the right data to be projected, because it doesn’t necessarily pick up the right columns right away. The report it creates, may be good enough, if you just need to quickly check what is happening. However, the report it creates, isn’t visually as exquisite or informative as one created by an expert. Also, it only offers a quick summary of the data, whereas a human can create multi page report offering deep understanding of the matter. You can also change the type of the visualizations in the automatically created report, but it is as simple to build the report from scratch. If you want to build a presentable report with the help of “auto-create report” -feature, you have to put as much thought and effort on it as if you were to build the whole thing from scratch.

In conclusion, we think that this feature is nice add to Power BI, because anyone can easily check the insights that the data has to offer and make decisions based on that information. Anyway, if you want to create a report that offers powerful support for your presentation, you still need to use some time on building the report and empathizing the major data points.

Future of AI Analytics

Even though the quality of the automatically created report isn’t yet quite as insightful as the report created by human expert, it still is impressive, how well it can connect different types of data and produce meaningful visuals all by itself. AI and machine learning technologies have been rapidly evolving in recent years and Data Analytics offers great usage for those. They are already great at identyfying patterns and analyzing the relationships and dependencies between variables. Therefore, we believe that there is still room for this “auto-create report” -feature to improve. In the future, it might be able to interpret and communicate the information hidden in the data even better than the brightest expert.

At the moment, the trend seems to be that we are trying to advantage AI by using generative AI language models as a trusted helper that will do the hand-on work for us. Microsoft has informed us about the copilot feature which will be included in the Fabric offering but isn’t available yet in the public preview version. They have showed us how you’ll be able chat and tell what information you want to know from the data. It can create measures and SQL views. Of course, it can create visuals, but it can answer more sophisticated questions too. For example, it can show you with visuals the reasons why something has happened or give suggestions via chat on how you could improve certain values. With the copilot, the only thing left for humans to do, is to know what to ask. Often those questions repeat themselves so maybe we might be able to automate also that task someday.

Microsoft Fabric

Posted on June 20, 2023October 21, 2025

You might have noticed the buzz around Microsoft’s recently announced service offering called Microsoft Fabric. So have we! In this article we take a closer look at the product and discuss if it is worth the hype.

Etlia Data Engineering announces completion of personnel share offering

Posted on April 28, 2023October 21, 2025

Etlia Ltd

News release

28 April 2023 – 09:00 EET

Etlia Data Engineering has today closed it’s first personnel share offering. All Etlia’s employees participated in the offering with full subscription rights making all the employees also shareholders of the Company.

“Our personnel offering was 100% success! I am thrilled to see such engagement and interest into our share offering. I am proud that now all our employees are also Etlia’s shareholders. Our intention is to continue personnel offerings also in the coming years alongside our partner program which was launched this year.” says Juuso Maijala, CEO.

“It is fantastic to see the huge enthusiasm of Etlians and their commitment into company’s growth journey. Using ITA66a§ (Finnish: TVL66a§) framework provides an excellent way to engage personnel and I can recommend it to any company seeking to boost it’s growth through a share based incentive program.“ says Mikko Koljonen, Board Member.

Additional information:

Juuso Maijala, CEO

juuso.maijala@etlia.fi

+358 50 532 0157

Mikko Koljonen, Board Member

mikko.koljonen@etlia.fi

+358 50 36 28 218

Etlia Ltd shortly:

Etlia is a data engineering company.

We help our customers create business value from data by leveraging major business process platforms and external sources. We offer top experts the best platform and community to grow professionally. Our company was founded in 2013. We are based in Espoo, Finland.

Synapse vs Databricks: A Comparison

Posted on April 13, 2023October 21, 2025

From Databricks to Synapse: A Data Architect’s Journey

As a Data Platform Architect/ Engineer working with several clients in Finland, I have extensive experience using Azure Databricks and Azure Data Factory (for notebook orchestration). Recently, however, one of my clients made the decision to switch to Azure Synapse Analytics. In this post, I will share my journey of transitioning from Databricks to Synapse and provide insights that may help you make a more informed decision if you are considering either of these platforms.

When it comes to choosing between Synapse and Databricks for your data processing needs, there are several factors to consider. Firstly, we will take a closer look at some of the key features of each platform and then finally my opinion on the matter.

Data Storage, Resource Access, and DevOps Integration

When comparing Databricks and Synapse, it is important to consider the availability of certain features. For example, Databricks allows you to use multiple notebooks within the same session – a feature that is not currently available in Synapse. Another key difference between the two platforms is the way they handle data storage. Databricks provides a static mount path for your storage accounts, making it easy to navigate through your data like a traditional filesystem. In contrast, Synapse requires you to provide a ‘job id’ when reading data from a mount – an id that changes every time a new job is run.

When it comes to accessing resources, Synapse offers linked service access management – a feature that allows for cleaner and more manageable connections between different services via Azure. In contrast, Databricks relies on tokens generated by service principals for resource access. However, Databricks does have an advantage when it comes to bootup time – boasting faster speeds than Synapse. On the other hand, Synapse has better DevOps integration compared to Databricks.

Features, Performance and Use Cases

There are several other key differences between Databricks and Synapse that are worth considering. For example, Databricks currently offers more features and better performance optimizations than Synapse. However, for data platforms that primarily use SQL and have few Spark use cases, Synapse Analytics may be the better choice. Synapse has an open-source version of Spark with built-in support for .NET applications, while Databricks has an optimized version of Spark that offers increased performance. Additionally, Databricks allows users to select GPU-enabled clusters for faster data processing and higher concurrency.

User Experience

In terms of user experience, Synapse has a traditional SQL engine that may feel more familiar to BI developers. It also has a Spark engine for use by data scientists and analysts. In contrast, Databricks is a Spark-based notebook tool with a focus on Spark functionality. Synapse currently only offers hive metadata GUI but with Unity Catalog, Databricks takes it to another level of creating the metadata hierarchy.

Managing Workflows with External Orchestration Tools

One important aspect to understand when using notebooks in Databricks is the lack of an in-built orchestration tool or service. While it is possible to schedule jobs in Databricks, the functionality is quite basic. For this reason, in many projects we used Azure Data Factory to orchestrate Databricks notebooks. In a recent Databricks meetup, one participant mentioned using Apache Airflow for orchestration on AWS – though I am not sure about GCP. This is a crucial point to consider because Synapse bundles everything under one umbrella for seamless integration. Until Databricks produces an alternative solution, you will need to use it alongside ADF (Azure Data Factory) or Synapse for orchestration.

Feature	Databricks	Azure Synapse Analytics
Multiple notebooks within same session	Yes	No
Data storage handling	Static mount path for storage accounts	Requires ‘job id’ when reading data from a mount
Resource access management	Tokens generated by service principals	Linked Service access management
Bootup time	Faster speeds than Synapse	Slower speeds than Databricks
DevOps integration	Less integration compared to Synapse	Better integration compared to Databricks
Features and performance optimizations	More features and better performance optimizations than Synapse	Fewer features and less performance optimizations than Databricks
SQL support	Less support for SQL use cases	Better support for SQL use cases
Spark engine	Optimized version of Spark that offers increased performance	Open-source version of Spark with built-in support for .NET applications
GPU-enabled clusters	Allows users to select GPU-enabled clusters for faster data processing and higher concurrency	Not available in Synapse now.
User experience	Spark-based notebook tool with a focus on Spark functionality	Traditional SQL engine that may feel more familiar to BI developers. Also has a Spark engine for use by data scientists and analysts.
Real-time Co-Authoring	Databricks Notebooks has as real-time co-authoring (both authors see the changes in real-time)	Synapse Notebooks has co-authoring of Notebooks, but one person needs to save the Notebook before another person sees the change
Orchestration tool or service	Lacks an in-built orchestration tool or service. Needs to be used alongside ADF or Synapse for orchestration.	Bundles everything under one umbrella for seamless integration.

Synapse vs Databricks feature comparison summary table.

Choosing Between Databricks and Synapse: Which One Is Right for You?

Ultimately, the choice between these two platforms will depend on your specific needs and priorities. Nah! I will not leave you with a diplomatic answer. In my opinion (could be controversial based on your cloud bias and when are you reading this) if your infra is on AWS/GCP, your priority is data processing efficiency and access to latest spark and delta features go for Databricks.

On the other hand, if your infrastructure is primarily based on Azure and your use case involves data preparation for a data platform with data modeling on a Datalake (reach out if you are interested to know how), then Azure Synapse may be the better choice. Synapse has more features in development for future releases – something that has not been announced by Databricks yet. Good luck! And stay tuned for upcoming series focusing on ML, streaming, delta and partitioning.

The Career Radar coaching program helps to clarify career goals

Posted on March 30, 2023October 21, 2025

In autumn 2022, Etlia started the Career Radar coaching program, which aims to provide all Etlia’s employees the opportunity to reflect on their current situation, strengths, and areas of development. It also enables discussing career ambitions with an external coach. In Career Radar the goals can be related to all kinds of things and do not have to match the company’s objectives. The employees also get to share their thoughts in a confidential environment. Based on the goals, they set up concrete steps for the future. Among Etlia’s personnel, the program has so far gained positive feedback.

The coaching helps to structure thinking

Etlia’s Senior Data Architect & Engineer Asko Ovaska participated in the Career Radar program and has been satisfied with the insights he has gained. Ovaska says the coaching was a great way to structure his thoughts and get a better idea of what he wants to do in the future. Sparring with an external coach also felt meaningful.

“I have had certain thoughts about what I would like to do in the future and what kind of career goals I have. For the first time, I could gather these issues in one place. The coach also gave some good insights and counter-questions. When going through them, I better recognized what is essential,” says Ovaska.

After the coaching session, Ovaska discussed the insights he had gained with his supervisor. He sees that going into the conversation was easier after having the chance to structure and write down thoughts and questions about his career. The next step of the program will take place in half a year, and the career objectives will be reviewed again with the external coach. Overall, Ovaska sees Career Radar as a positive experience and views it as more versatile and comprehensive compared to a traditional HR discussion.

From Career Radar towards the partner program

Etlia’s Senior Cloud Consultant & Data Engineer Raaju Srinivasa Raghavan also participated in the Career Radar coaching. Raaju sees that the coaching helped clarify what his future career path could look like. Although Career Radar gives free hands to realize all kinds of career dreams, Raaju’s objectives are closely related to Etlia, and with the coaching, the idea of applying to the company’s partner program became clearer.

”I had the session with the Career Radar coach and it was quite an eye-opening experience. I have been working on the technical side, but during the program, I was able to understand that the next steps would be more toward the partner track. It was helpful personally and from the company’s point of view.”

Raaju is happy that he can now carry forward the goals set in the training, and they have not remained mere words.

”Recently, we have kickstarted the partner track, so it is really nice to see that you don’t just speak about these things, but they are actually put into action.”

Raaju sees that the Career Radar concept is very much in line with Etlia’s values, one of which is empowering people. In his opinion, Career Radar is suitable for an organization where the atmosphere is open and caring, and people are also ready to receive feedback.

” It is not just about you and the company but more about what the company can do for you. Career Radar is a very nice initiative and possible to execute only in a company like Etlia where we are a close group of consultants, and we care about each other.”

CEO’s review of Etlia’s year 2023

Posted on February 16, 2023October 21, 2025

The year 2023 has already moved towards February, and we thought that now is a great moment to take a look at Etlia’s upcoming year with our CEO Juuso Maijala.

In 2022, Etlia’s financial goals were achieved and operations were based on our values

Before we turn to the future, let’s take a quick look at Etlia’s highlights from last year.

In 2022, Etlia reached many significant milestones, and the year contained many memorable events. Etlia exceeded one million euros in turnover for the first time, surpassing the goal of 1.1 million euros set at the beginning of the year.

We have acted based on our values. Above all, let’s mention our “we appreciate people” value, which is the basis of all our activities. Concrete actions in which our values were visible last year were especially the launch of the new employee-oriented career coaching program Career Radar, and the personnel offer we prepared, which will take place this spring.

In 2022, we made profitable growth, improved internal processes, and made new successful recruitments. We also managed to increase our recognition, which can be seen in the considerable growth of our Linkedin follower base.

The Year 2023 started with new goals

Let’s move on to this year. The first month started in a hurry but in a good mood. The goals for 2023 are high, and a lot has already been done in January to achieve them.

The goal, like last year, is to continue developing our work community and workplace culture. Etlia is and wants to continue being a people-oriented company in the future, which will guide what we do and is reflected in all our activities.

“We want to build a very open culture, where all the issues and the basis of decision-making are known to all etlians”, Juuso Maijala sums up.

The number of Etlia’s personnel is also intended to increase, which of course is also reflected in our business goals. We will also brighten up our brand, which can already be seen in the form of changes in the graphic look on our website. There are also new things to come in Etlia’s offering, and we will release a new service this year. However, we will tell you more about this a little later, so stay tuned!

In the big picture, Etlia’s strategic goals are unchanged, only minor changes were made to them in the strategy update at the end of the year. We want to find skilled data and analytics experts to join us to grow profitably and build Etlia’s recognition.

Unfortunately, the business of many sectors has suffered due to the crises of recent years, and they are also factors for us that should not be ignored. However, the demand for data and data expertise continues and increases every year, so Maijala believes that the growth of the industry will also continue in 2023. He sees the development of our industry in a very positive light, which is why Etlia dares to set high goals.

2023: Etlia’s 10th anniversary

Our company turns 10 years old this year, which will be reflected in many events and perhaps also surprises during the coming year. In honor of the anniversary, we will organize events for both staff and customers. Accordingly, we will celebrate our birthday with a proper birthday party.

Fast access to SAP ERP demo data sources

Posted on November 9, 2022October 21, 2025

After being sidelined on sick leave for the first half of the year it is time to dig deep into some neat tech stuff once again.

SAP ERPs being so prevalent in the large enterprise sector in Finland I thought we could investigate how to deploy, provision and kick up an SAP ERP system with ready demo data. We will use this demo data as a data source in the following parts of this series of blog posts.

And why would we want to do that, you may ask? 🤔

Well, once we have our ERP set up in the cloud of our choice, we can use it as a data source to for example test the AecorSoft Turbo loading speeds or proof the SAP CDC (Change Data Capture) capabilities for Azure Data Factory and Azure Synapse which just made it into General Availability.

High level steps:

Cloud Appliance selection
Provision to chosen Azure Region

Do check the full set of news from Microsoft Ignite 2022 by the way: https://news.microsoft.com/ignite-2022-book-of-news/

1. Cloud Appliance selection

Let us introduce you to the SAP Cloud Appliance Library in case it is new to you.

You can access it at cal.sap.com and the intro text there says it all:

“SAP Cloud Appliance Library offers a quick and effortless way to create SAP workloads in your cloud infrastructure. With a few clicks, you can set up a fully configured demo environment or deploy a standardized system layout for an SAP product based on default or custom SAP software installation stacks.”

Create your account, log in and dive into the Appliance Templates available. You can get going on a trial basis to test various systems.

Systems range from state-of-the-art S/4HANA ERPs to BW/HANA and older incarnations of these.

We will not be kicking up an S/4 with its memory-intensive requirements since that will be outside the budgetary range of this blog series 😊

Instead, we will filter the list with IDES to find SAP IDES (Internet Demonstration and Evaluation System) versions with ready data in them to use in our next steps down the line.

When you choose your appliance and hit Create Appliance you can choose your cloud provider from Azure, AWS, or GCP. In this case, we will use our Azure partner subscription and authorize SAP CAL against that for it to be able to provide? the resources there.

2. Provision to chosen Azure Region

When using the trial, we are unfortunately limited to only using Azure West Europe region.

It would have been nice to use the announced Azure Kirkkonummi Data Center in my backyard, but that will still take some digging and building. https://news.microsoft.com/fi-fi/2022/03/17/microsoft-rakentaa-suomeen-datakeskusalueen/ I cannot wait to get my own data center and sustainable heating as well! 😊

The next best option would have been Azure Sweden Central with its lower latency:

https://www.azurespeed.com/Azure/Latency

https://azure.microsoft.com/en-us/explore/global-infrastructure/products-by-region/?products=data-factory,synapse-analytics,databricks

Unfortunately, some of the other services we would like to utilize in our future scenarios are not yet available in that region, so we are not heading there yet.