Dileep Pournami

The year is 2033. Companies who pursued a data mesh strategy starting in 2022 pontificate about what the next 10 years of their journey looks like: Expected wins, expected hurdles, and new dynamics they expect to enter their data world. In this panel discussion, hear from Sachin Menon from Priceline, Bryan Aller from Comcast, and Dileep Pournami from NatWest – who have already begun their data mesh journey. You’ll hear quick lightning talks from each company on their “Why” for data mesh, and… Show more

The year is 2033. Companies who pursued a data mesh strategy starting in 2022 pontificate about what the next 10 years of their journey looks like: Expected wins, expected hurdles, and new dynamics they expect to enter their data world. In this panel discussion, hear from Sachin Menon from Priceline, Bryan Aller from Comcast, and Dileep Pournami from NatWest – who have already begun their data mesh journey. You’ll hear quick lightning talks from each company on their “Why” for data mesh, and “Where” they are today, and then we’ll have a discussion about what the next 10 years might look like. Show less

Panel discussion on data engineering at scale with Stream Sets CEO Girish Pancha and IDC Research Manager Giovanni Cervellati

The Hub and Spoke model for data engineering is an emerging trend in the industry where more autonomy and self-service is enabled for multiple data engineering teams to tackle business problems in a ‘federated way’. A centralised HUB team focusses more on the platform engineering and implementation of services to enable this.

This does reduce the risk of central team bottlenecks, however, does provide another level of technical and operating complexity to manage.

This talk covers… Show more

The Hub and Spoke model for data engineering is an emerging trend in the industry where more autonomy and self-service is enabled for multiple data engineering teams to tackle business problems in a ‘federated way’. A centralised HUB team focusses more on the platform engineering and implementation of services to enable this.

This does reduce the risk of central team bottlenecks, however, does provide another level of technical and operating complexity to manage.

This talk covers how the central platform team in NatWest used best of breed solutions from technologies like Transformer and Spark and added on exemplars, APIs and other library utilities to make data engineering easy and ‘fun’ for Spoke teams.

The add-ons include a customised ‘Data Engineering SDK’ based on Project Hydra, a Kafka based loosely coupled choreography framework called ‘Watch Tower’, and several APIs for hard to achieve ‘HUB’ tasks like on-prem to cloud replication. Show less

Talk 1: Participated in the Data Ops practitioner panel discussion
Talk 2: Break out session about the first real time analytics use case in NatWest.

At the Royal Bank of Scotland, one of our key organizational design principles is to "share everything we can share". In essence, this promotes the cross-departmental sharing of platform services. Historically, this was never enforced in our Business Intelligence platforms like SAS®, resulting in a diverse technology estate, which presents challenges to our platform team for maintaining software currency, software versions, and overall quality of service. Currently, we have SAS® 8.2 and SAS®… Show more

At the Royal Bank of Scotland, one of our key organizational design principles is to "share everything we can share". In essence, this promotes the cross-departmental sharing of platform services. Historically, this was never enforced in our Business Intelligence platforms like SAS®, resulting in a diverse technology estate, which presents challenges to our platform team for maintaining software currency, software versions, and overall quality of service. Currently, we have SAS® 8.2 and SAS® 9.1.3 on the mainframe, SAS® 9.2, SAS® 9.3, and SAS® 9.4 across our Windows and Linux servers, and SAS® 9.1.3 and SAS® 9.4 on PC across the bank. One of the benefits to running a multi-tenant SAS environment is removing the need to procure, install, and configure a new environment when a new department wants to use SAS. However, the process of configuring a secure multi-tenant environment, using the default tools and procedures, can still be very labor intensive. This paper explains how we analyzed the benefits of creating a shared Enterprise Business Intelligence platform in SAS alongside the risks and organizational barriers to the approach. Several considerations are presented as well as some insight into how we managed to convince our key stakeholders with the approach. We also look at the "custom" processes and tools that RBS has implemented. Through this paper, we encourage other organizations to think about the various considerations we present to decide if sharing is right for their context to maximize the return on investment in SAS. Show less