From SAP to Data Mesh

As the data ecosystem gained its momentum in the last ten years, more and more companies invested in becoming “data-driven” after the early adopters demonstrated how much value data could generate.

This “gold rush for data” focused on opening silos and centralizing data into platforms, lakes, pushing faster and faster towards distributed architectures, hybrid clouds, even transitioning from IaaS to PaaS to SaaS. But it lacked attention to aspects like data ownership, data integration, data quality assurance, scalable governance, usability, trust, availability and discoverability of data, which are the key factors that allow consumers to find, understand, and safely consume data to provide business value.

During the webinar, we discussed with Guido Pezzin (Qlik QDI TSM Italy), Roberto Coluccio (Agile Lab Big Data Architect) and Robert Zenkert (Qlik Analytics Data Architect) how a Change Data Capture strategy could be useful to replicate events from an existing SAP environment inside a data product, which is the basic quantum in a Data Mesh architecture.

If you missed the event, fill in the form and watch the video!


La piattaforma operativa di Agile Lab per Vodafone Automotive

Scienza e Tecnologia 

La soluzione realizzata da Agile Lab è basata sul framework Wasp (Wide Analytics Streaming Platform) , una piattaforma Data Streaming in grado di raccogliere e analizzare dati tramite logiche analitiche o machine learning, restituendo risultati…

Tech Business

La piattaforma di Agile Lab scelta per gestire i dati di Vodafone Automotive

La soluzione si basa sul framework WASP

Agile Lab ha realizzato per Vodafone Automotive, società del Gruppo Vodafone, una piattaforma applicativa in grado di raccogliere, analizzare, elaborare e salvare in maniera ottimizzata ingenti quantità di dati, migliorando, di fatto, il servizio ai clienti e aumentando il vantaggio competitivo dell’azienda…


Agile Lab e Vodafone Automotive: due aziende italiane per i big data delle flotte auto

Le due aziende italiane collaborano nella gestione ottimale dei big data generati dai clienti del settore automotive, elaborando grandi quantità di informazioni in tempi rapidi…

Channel Tech

Agile Lab: portare le multinazionali verso un mondo data driven

Creare valore, partendo dai dati, attraverso applicazioni sviluppate in ambito Big Data e Machine learning.

Agile Lab è un’azienda italiana nata nel 2014 per iniziativa privata di Alberto Firpo e Paolo Platter rispettivamente CEO e CTO, che si rivolge ai settori bancario, assicurativo, manifatturiero, utilities, conta oltre 50 persone distribuite nelle sedi di Torino, Milano, Treviso, Bologna, Catania e Bari…

Industria Italiana

Digitalizzazione: Intesa Sanpaolo Smart Care sceglie Agile Lab per ottimizzare i processi

Obiettivo della collaborazione è garantire ai clienti del Gruppo una migliore fruizione della gamma di servizi dedicati al mondo della salute, della mobilità e della casa.

«Siamo onorati di aver contribuito con la nostra tecnologia a migliorare l’offerta di servizi Intesa Sanpaolo Smart Care velocizzando i processi e supportando il Gruppo …

Data Mesh explanation

Data Mesh explanation 

How and why successful data-driven companies are adopting Data Mesh

Paradigm shift

Every once in a while, a new way of doing things comes along and changes everything. Sometimes this takes the form of new technologies, infrastructures, services. Some other times, the slack arises as an urgent need from the market itself. While the former needs engineering teams to push the change, the latter is very likely an “ask for help” straight from the business, and that’s the most powerful engine industry can have.

As the data-* (management, processing, governance, …) ecosystem gained its momentum in the last ten years, more and more companies invested in becoming “data-driven” after the early adopters demonstrated how much value data could generate. This wave enabled the development of all the Big Data and Cloud technologies/standards/services we very well know and use today.

This “gold rush for data” focused on opening silos and centralizing data into platforms, lakes, pushing faster and faster towards distributed architectures, hybrid clouds, even transitioning from IaaS to PaaS to SaaS. But it lacked attention to aspects like data ownership, data quality assurance, scalable governance, usability, trust, availability, and discoverability of data, which are the key factors that allow consumers to find, understand, and safely consume data to provide business value.

Data Mesh is a paradigm shift that arose as a need from “the fields” from the actual world of monolithic data lakes/platforms. It can be considered revolutionary for the results it promises and evolutionary, as it leverages existing technologies and is not bound to a specific underlying technology.

It is an organizational and architectural pattern leveraging domain-driven design, that is the capability of designing data domains that are very much business-oriented instead of being technology-oriented. We can see this paradigm shift on data as analogous to when monolithic web-services transitioned to domain-driven-designed micro-services.

When Data Lake becomes Data Mess

To better understand the main advantages of Data Mesh and its architectural principles, we need to take a step back and look at what was (and in most cases still is) state-of-the-art for data management before this new paradigm.

In the last years, data management’s main trend has been to create a single, centralized Data Lake (often built on-premise) to achieve both centralized data governance and a centralized processing platform. While the former proved successful, despite significant technology investments, the latter became counterproductive, both from organizational and technical points of view, for several reasons.

When creating Data Lakes, the first mantra was to open the silos, which meant setting up as soon as possible ingestion pipelines to bring data from the external systems to the data lake. The data lake’s internal data engineers team usually had the accountability to design these processes. The integration effort was undertaken from a systems point of view, i.e., let’s understand how we can take data in external systems and bring it into the data lake. This happened to occur via the broadest variety of special-purpose or generalized ETL (Extract, Transform, Load) jobs or CDC (Change Data Capture) tools. Once the integration was set up, the data ownership fell automatically in the hands of the data engineering team, who usually did not pay so much effort in first agreeing with the source systems on data documentation, data quality, etc., thus resulting in extra effort to implement checks, metrics, data quality measurements on “not-so-well-known” data.

This integration-based approach leads to even worse scenarios when something about the source system mutates: schema changes, source domain specifications evolving, GDPR introduction, you name it … It is a model that cannot scale up, especially for multinational corporations centralizing data from different branches/countries and related laws/regulations, because source systems are not aware of the process of data warehousing, they don’t know about data consumer needs, they are not focused on providing data quality on their data because it’s not their business purpose. This usually sets the scene for disengagement in creating added value for the overall organization.

Another classical problem of Data Lake is the layered structure, where layers are typically technical (cleansing, standardization, harmonization). You can look at these layers as a fixed amount of overhead between data and the business needs that are continuously slowing down the process of value creation.

Data Mesh overview

Data Mesh is now defined by 4 principles (according to Zhamak Dehghani): 

  • Domain-oriented decentralized data ownership and architecture 
  • Data as a product 
  • Self-serve data infrastructure as a platform 
  • Federated computational governance. 

To understand what is changing compared with the past, it is useful to start by changing the vocabulary. In Data Mesh, we talk more about serving than ingesting, as it is more important to discover and use data rather than extract and load it. 

Every movement or copy of the data has an intrinsic cost: 

  • Development: the ETL must be developed, tested, and deployed 
  • Maintenance: this is the worst one. You need to monitor such processes, adapt them when the sources are changing, take care of data deletion, dispersion of data ownership. 

Often the data movement or copy is needed for the following reasons: 

  • Technical layers 
  • Technology needs: you have your data on S3, but SAP requires having the data in an internal table to process it. Or you have a massive dataset on Redshift, and your ML training tool is requiring data on S3. 
  • No time travel and history capabilities: need to snapshot a data source 

Keep in mind that data movement/copy is not data denormalization. Denormalization is quite normal when you have multiple consumers with different needs, but this does not imply a transfer of ownership.  

When you move data from a system/team to another, you transfer the ownership, and you are creating dependencies with no added value from a business perspective. Data Mesh transfers data ownership only when data is assuming a new functional/business meaning. 

Data Mesh paradigm is also instrumental in “future-proofing” the company when new technologies emerge. Each source system can adopt them and create new connectors to the scaffolding template (we will go deeper on this in the next articles), thus maintaining coherence in providing access to their data for the rest of the company through Mesh Services. 

Data Mesh adoption requires a very high level of automation concerning infrastructure provisioning, realizing the “so-called” self-service infrastructure. Every Data Product team should be able to autonomously provision what it needs. Even if teams are to be autonomous in technology choices and provisioning, they cannot develop their product with access to the full range of technologies that the landscape offers. A key point that makes a data mesh platform successful is the federated computational governance, which allows interoperability through global standardization. The “federated computational governance” is a federation of data product owners with the challenging task of creating rules and automating (or at least simplifying) the adherence to such regulations. What is agreed upon by the “federated computational governance” should, as much as possible, follow DevOps and Infrastructure as Code practices. 

Each Data Product exposes its capabilities through a catalog by defining its input and output ports. A Data Mesh platform should nonetheless provide scaffolding to implement such input and output ports, choosing technology-agnostic standards wherever possible; this includes setting standards for analytical, as well as event-based access to data. Keep in mind that it should ease and push the internally agreed standards, but never lock product teams in technologies cages. The federated computational governance should also be very open to the change, letting the platform evolve with its users (product teams). 

Data Product standardization is the foundation to allow effortless integration between data consumers and data producers. When you buy something on Amazon, you don’t need to interact with the seller to know how to purchase the product or know which characteristics the product has. Product standardization and centralized governance are what a marketplace is doing to smooth and protect the consumer experience.


On this topic, you might be interested in the 10 practical tips to reduce Data Mesh’s adoption roadblocks we designed at Agile Lab, or you can learn more about how Data Mesh boost can get your Data Mesh implementation started quickly. 


If you made it this far and you’re interested in other articles on the Data Mesh topics, sign up for our newsletter to stay tuned. Also, get in touch if you’d like us to help with your Data Mesh journey.

Managed Services for Mission Critical Big Data environment

Managed Services 

for Mission Critical Big Data Environment

A custom solution of data managed services, adopting the discipline of Site Reliability Engineering (SRE), which incorporates aspects of software engineering and applies them to infrastructure and operations problems, with the main goals of creating scalable and highly reliable software systems.

To understand how, watch the video recorded during a past webinar (in Italian).

Bright Ideas Gone Agile

Bright Ideas Gone Agile

Reforming our Brand Identity: 

The Story Behind Agile Lab’s Logo

Today, the launch of our new website marks the beginning of a new phase for Agile Lab. In addition to the site, we have redesigned our logo to reflect the positive changes our company has experienced over the last few years. Founded in 2014, Agile Lab was born as a tech start-up with a forward-looking vision and ambitious plans, and has come a long way since. As the company continues to grow and evolve, our brand identity is growing and evolving along with it. This is why we couldn’t be more proud to introduce our brand-new logo, reflecting the innovation and foresight that Agile Lab stands for.  


Since the creation of our former logo, things have been progressing fast, ideas have been put into action and the company’s reach has expanded at rocket speed. The iconic blue cloud with a lightbulb at its core, has been transformed accordingly to our ongoing progress. We have been rethinking our design into an image that incorporates the company’s past, present and future. The restyling we’ve worked on represents the constant evolution of our company, as well as its remarkable rise. Our new logo is one that values Agile Lab’s legacy, whilst looking at its future accomplishments and breakthroughs. The still cloud has turned into a more abstract and agile symbol.  


To us, the geometry of two intersecting circles is open to multiple interpretations: the ability to capture current technology trends and foresee future ones; the ability to build new partnerships while maintaining and growing previously established relations; and most importantly, it represents the dynamic system behind the processing, extraction and transformation of Big Data. Essentially, through our new branding, we aim to communicate the commitment and hard work of a visionary brand that is projected into the future of innovation and data science. Needless to say, our achievements and aspirations would never be possible if it weren’t for the synergy and efforts of a great team that strives each and every day to turn bright intuitions into software and provide concrete services and solutions to our clients. Nor would we have made it thus far without the amazing support of our clients, who believed in our skills and committed to our shared projects.  


Agile Lab is moving forward by the day. This month, we have celebrated the milestone of appearing on the Financial Times FT-1000 European Fastest-Growing Companies list, and we are looking forward to seeing what the future has in store for us. Please follow us on our channels – Instagram, You Tube, Twitter and LinkedIn -, and we will keep you posted on any further progress of our team – and just a heads-up, our newsletter is coming soon, so please stay tuned for more content from Agile Lab!