PyData Yerevan 2022

Modern Data Stack: Optimising and Scaling Data in a tech company
08-13, 16:15–16:55 (Asia/Yerevan), 114W PAB

A new approach to data integration that Dataops can enable to save engineering time, allowing engineers and analysts to pursue higher-value activities.

The modern data stack (MDS) is a suite of tools used for data integration. These tools include, in order of how the data flows:

a combination of fully managed ELT Pipeline + custom managed ETL pipeline a cloud-based columnar warehouse or data lake as a destination a data transformation tool a business intelligence or data visualisation platform.

The data warehouse it is seen as a platform for reporting and analytics. But as the center of gravity for data shifts towards the data warehouse, data warehouses should be leveraged by end users with a quality control, engineering best practices mindset to solve or diagnose common business problems.

The “modern data stack” is positioned as the current best option for organisations to aim for, and the end point of, transformation projects. But the modern data stack isn’t just a collection of diagrams; it’s an experience and a never-end journey for tech organisations.

This talk is about a new approach to data integration that DataOps is enabling in tech companies (with Sololearn practical example) to save engineering time, allowing engineers and analysts to pursue higher-value activities, explaining why every tech organisation should have a Data + Analytics Engineering (DataOps) department.

It answers two main questions: Why should we care about the adoption of data + analytics engineering? What are the steps and processes to start the journey to full adoption?

This talk will look at themes around that journey: metadata, tools, & organisation action points, to paint a picture of what the next phase of the journey looks like. We will go through the modern data stack. We will talk about its architecture, which tools fit where, and how to organise teams to support it. We’ll also touch on the challenges in operationalising warehouses, and discuss future technology advancements that could unlock the warehouse to become the platform for business needs and operations.

Prior Knowledge Expected

No previous knowledge expected

I believe in data’s power to identify solutions for the biggest challenges and as unique enabler for business stakeholders to achieve sustainable growth. I have rising experience in creating and leading high-performance teams in the engineering and fintech industries. I created and managed a Data team from scratch, designing and implementing the whole Business Intelligence and ML-ops cloud infrastructure.

As a data professional I am passionate about analysing and integrating data from different sources to design tools and specialised analytics to drive strategic decisions. To further leverage disruptive technologies such as AI and Machine Learning approaches to optimise the relationship with people and customers.

My mission is to be the ally of those who have the challenge to make an impact, and give them the tools to understand performance and what users love.