Modern Data Stack

A modern data stack

Building a Modern Data Stack

Your organization understands the value of data, but struggles to put the right data in the hands of the right people at the right time. A modern data stack is essential in your organisation when you want get value out of all your data. To build a modern data stack you need a data warehouse that allows you to build data sets that are: up to date, reliable, maintainable, documented, well-governed and ready for the cloud. But also, intelligent tools and the right skills in your team are needed for a foundation on which you build an organization where data democratization is a reality.

Article- Making data available and accessible to the entire organization

Learn more about the right data, at the right time, in the right place

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Ending of the Traditional Data Warehouse

The traditional data warehouse is moving to the cloud and a new stack of tools, often driven by open source initiatives have changed the playing field beyond recognition. Until recently, data was “owned” by IT departments. Business units such as marketing, used the data to make business decisions, but they always had to go through the IT department to get the data. The goal of data democratization is to have anybody use data at any time to make decisions with no barriers to access or understanding. Data needs to be generally available and a modern data warehouse enables that data democratization. A radical paradigm shift has started in the Business Intelligence (BI) space and the end of the traditional data warehouse is near, ready to be replaced by more relevant architectures that can be deployed on the cloud within the blink of an eye.

The Modern Data Stack

A modern data stack is centered on a powerful data warehouse. Data is loaded directly into the warehouse. A robust and reliable transformation layer is used to turn that raw data into dependable and meaningful datasets. As a company we have partnered with many key players in the ecosystem and below we give suggestions for solutions to start using in your organization.

Cloud Data Platform: Azure, AWS, GCP
Modern data warehouse: Amazon Redshift, Google BigQuery, Snowflake, DataBricks
Business Intelligence: Tableau, PowerBI. Looker
Transformation and Orchestration: Apache Airflow, DBT
Data Ingestion: Fivetran, Stitch

We can help you make the best selection possible based on the unique setup at your organization.

Allows you to build data sets

The Modern Data Team

As data tools change, so do the people who use them. Business users want to self-serve and be data-driven so the data has to be ‘ready’ to use. Recently, we introduced the role of Analytics Engineer to act as the bridge between data engineers and data analysts. It is their job to build well tested, up to date and documented datasets that the rest of the company can use to answer their own questions. They are technical enough to easily be able to apply software engineering best practices like version control and CI/CD but also need to be able to communicate effectively with stakeholders to teach them about these things.

Doing Analytics right, by getting the right data in the hands of the right people at the right time. Fully automated and Iteratively!

SOME OF OUR TECHNOLOGY CONTRIBUTIONS

Data engineers

Manage the generic data infrastructure to make the raw data available and accessible across an organization.

Data analysts

Partner with business stakeholders to answer questions with data, build dashboards and reports, and to carry out exploratory analysis.

Data scientists

Use statistics and machine learning to build models for optimization, forecasting, or outlier detection to be used in products or services and continuously improving those through experimentation.

Machine learning engineers

Create smart, machine-learning driven products in production and understand what it takes go from the laptop of the data scientist to running 24/7 in a secure, robust environment.

Cloud engineer

Builds a foundation build from native cloud services in order to safely and securely deploy products on a cloud platform.

Why an Analytics Engineer

A bridge between data engineers and data analysts

The role of analytics engineer recently emerged because organizations now understand the value of data-driven decision making and you have trained domain experts to become analysts and hired data engineers to make the data readily available. However, there is often not enough synergy between the two roles.

Data Engineers

Focus on building a solid foundation for the data infrastructure that can be used by all domains within your organization. In this way, they make it easier for others to build solutions for their own, specific data use cases and use tools that fit their experience and needs.

Analytics Engineer

Improve the accuracy, reliability, security and speed of delivery of analytics workflows. Speed up the organization-wide adoption use self-service analytics. Start a sane, agile data governance that focuses on value, quality and use of data instead of on process and roles.

Data Analysts

Combine in-depth business knowledge and data skills to support decision making in your organization with reports and visualisations. For that they need to be able to work with aggregated data of enough quality and have the tools to continue to combine, aggregate and improve.