[This is a post-conference workshop back to back to our Data Warehousing & BI Summit where Mike Ferguson will deliver a keynote as well. You can register for this workshop separately via this page or, with a discount, in combination with the conference via the conference registration form.]
Most companies today are storing data and running applications in a hybrid multi-cloud environment. Analytical systems tend to be centralised and siloed like data warehouses and data marts for BI, cloud storage data lakes for data science and stand-alone streaming analytical systems for real-time analysis. These centralised systems rely on data engineers and data scientists working within each silo to ingest data from many different sources and engineer it for use in a specific analytical system or machine learning models. There are many issues with this centralised, siloed approach including multiple tools to prepare and integrate data, reinvention of data integration pipelines in each silo and centralised data engineering with poor understanding of source data unable to keep pace with business demands for new data.
To address these issues, a new approach called Data Mesh emerged in late 2019 attempting to accelerate creation of data for use in multiple analytical workloads. Data Mesh is a decentralised business domain-oriented approach to data ownership and data engineering to create a mesh of reusable data products that can be created once and shared across multiple analytical systems and workloads.
This half-day workshop looks at the development of data products in detail and also, how can you use a data marketplace to share and govern the sharing of data products across the enterprise to shorten time to value.
Learning Objectives:
Who is it for?
This seminar is intended for business data analysts, data architects, chief data officers, master data management professionals, data scientists, IT ETL developers, and data governance professionals. It assumes you understand basic data management principles and data architecture plus a reasonable understanding of data cleansing, data integration, data catalogs, data lakes and data governance.
Van der Valk Hotel Utrecht
Winthontlaan 4-6
3526 KV Utrecht
Telefoon 030 8000 800
The hotel is very well accessible by public transport. From busstop ‘Kanaleneiland Zuid’ it is only a three-minute walk. You can take buses 63, 65, 66, 74 and 77 from Utrecht Central Station and you also take the tram line 20 or 21 from the train station and get off at stop ‘Kanaleneiland’. Please consult www.9292.nl (door-to-door journey planner, also available in English) or call 0900-9292 (travel advice by phone, € 0.70 p/m).
Van der Valk Hotel Utrecht is also located next to the highway A12, exit 17 (Utrecht / Jaarbeurs / Kanaleneiland).
Although the hotel has a large parking garage, we cannot guarantee parking spots. We therefore advise you to go by public transport.
For those who would like to arrive the day before, there is the possibility of staying at the Van der Valk Hotel Utrecht. However, the hotel does not provide special discounts for attendees of events. Therefore, when interested in an overnight stay, please consult Van der Valk directly to make a reservation.
More information about the hotel and the location can be found on their website www.vandervalkhotelutrecht.nl.
The course starts at 09:00 am and ends at 12:30. Registration commences at 08:30 am.
Detailed Course Outline
Most companies today are storing data and running applications in a hybrid multi-cloud environment. Analytical systems tend to be centralised and siloed like data warehouses and data marts for BI, cloud storage data lakes or Hadoop for data science and stand-alone streaming analytical systems for real-time analysis. These centralised systems rely on data engineers and data scientists working within each silo to ingest data from many different sources, clean and integrate it for use in a specific analytical system or machine learning models. There are many issues with this centralised, siloed approach including multiple tools to prepare and integrate data, reinvention of data integration pipelines in each silo and centralised data engineering with poor understanding of source data unable to keep pace with business demands for new data. Also, master data is not well managed.
To address these issues, a new approach emerged in late 2019 attempting to accelerate creation of data for use in multiple analytical workloads. That approach is Data Mesh. Data Mesh is a decentralised business domain-oriented approach to data ownership and data engineering to create a mesh of reusable data products that can be created once and shared across multiple analytical systems and workloads. A Data Mesh can be implemented in a number of ways. These include using one or more cloud storage accounts on cloud storage, on an organised data lake, on a Lakehouse, on a data cloud, using Kafka or using data virtualisation. Data products can then be consumed in other pipelines for use in streaming analytics, Data Warehouses or Lakehouse Gold Tables, for use in business intelligence, feature stores for use data science, graph databases for use in graph analysis and other analytical workloads.
This half-day workshop looks at the development of data products in detail. It also looks at the strengths and weaknesses of data mesh implementation options for data product development. Which architecture is best to implement this? How do you co-ordinate multiple domain-oriented teams and use common data infrastructure software like Data Fabric to create high-quality, compliant, reusable, data products in a Data Mesh. Is there a methodology for creating data products? Also, how can you use a data marketplace to share and govern the sharing of data products? The objective is to shorten time to value while also ensuring that data is correctly governed and engineered in a decentralised environment. It also looks at the organisational implications of Data Mesh and how to create sharable data products for use as master data, in a data warehouse, in data science, in graph analysis and in real-time streaming analytics to drive business value? Technologies discussed includes data catalogs, data fabric for collaborative development of data integration pipelines to create data products, DataOps to speed up the process, data orchestration automation, data observability and data marketplaces.
Taking part in this workshop will only cost € 351 when registering 30 days beforehand and € 390 per person after the Early Bird period expires (excl. 21% Dutch VAT). This also covers documentation and lunch.
In completing your registration form you declare that you agree with our Terms and Conditions.
*) All pricing is VAT-excluded and EU VAT regulation stipulates that if you attend an event on-premise in The Netherlands, we are required to include local VAT. In case of discrepancy in registration fee between the website and the PDF brochure, the information on this page of the website always prevails.
Extra discounts
Discounts are available for group bookings of two or more delegates representing the same organization made at the same time. Ten percent off for the second and third delegate and fifteen percent off for all delegates when registering four or more delegates (all delegates must be listed on the same invoice).
This cannot be used in conjunction with other discounts.
Payment
Full payment is due prior to the event. An invoice will be sent to you containing our full bank details including BIC and IBAN. Your payment should always include the invoice number as well as the name of your company and the delegate name.
Payment by credit card is also available. Please mention this in the Comment-field upon registration and find further instructions for credit card payment on our customer service page.
Practically all of our seminars and workshops can be offered as an In-house course for your company exclusively. We can tailor with extra focus on specific topics that apply to your organization. Also available in online format or in face-to-face format with live video stream.