Data Engineering

Through data engineering methods, we design and build systems to collect, transform and load data to prepare it for further analysis.

What is Data Engineering?

Data engineering is based on building data pipelines to extract information from different sources. It converts information into clean data to be used in further processes such as data science, data visualization, and business analytics. Our goal is to provide organized, standard data flow to enable data-driven models.


As data engineers, we develop, construct and maintain architectures to process data in alignment with business requirements. Our methodology involves gathering data requirements, maintaining metadata about data, ensuring security and governance for the data, storing data, and processing data for specific needs. All is done in communication with our clients and in line with their needs, so the result is accurate and specific data ready for future analysis and utilization.

What do you get?

Elimination of bad data

Usage of relevant sources that bring the most value

Stable systems for data collection

A strong basis for further data usage and analysis

An optimal, precise, and solid system for data collection, flow, and storage

Areas of expertise

We approach data through the main extract, transform, and load process so it can be placed into a targeted repository. Through ETL we combine data from multiple data sources into a single, consistent data store that is loaded into a data warehouse or other target system.

With the data warehousing process, we collect and manage data from varied sources to provide meaningful business insights. We use data warehouse to connect and analyze business data from heterogeneous sources needed for analysis and reporting.

We create subsets of a data warehouse focused on a particular line of business, department, or subject area. With data marts, we make specific data available to a defined group of users. This allows users to quickly access critical and specific insights without wasting time searching through an entire data warehouse. It also improves the security of access since users can view only parts of the data intended for them. Furthermore, data marts secure better reading operations performances since they are optimized for specific users.

Through data lakes, we provide storage for structured or unstructured and raw data, at any scale, from multiple sources in one place. Data lakes store data in its native form before it is needed to run different types of analytics to guide users in making better business decisions. They are useful to data scientists and analysts for ad hoc analysis, and for discovering the possibilities that derive from collected data without the need to consolidate them in the data warehouse first.

Our approach

Pre-discovery

We do initial interviews with you to define your goals and expectations. Afterward, we do as is system analysis where we establish if you already have some data engineering processes implemented and in which conditions are they. We check the state of databases, data warehouses, data pipelines, and automation processes.

Discovery

Through detailed interviews, we set to define project goals and requirements. We create detailed specifications and plan for the implementation phase. The aim is to specify data sources, ETL process design, and business logic that connects data sources. Also, we define data warehouse design, solution architecture, and technology selection.

Implementation

In this phase, we set up the infrastructure and develop a full-scale solution. Next, we identify and recommend data analytics or data science possibilities once the data engineering part is done.

Pre-discovery

We do initial interviews with you to define your goals and expectations. Afterward, we do as is system analysis where we establish if you already have some data engineering processes implemented and in which conditions are they. We check the state of databases, data warehouses, data pipelines, and automation processes.

Discovery

Through detailed interviews, we set to define project goals and requirements. We create detailed specifications and plan for the implementation phase. The aim is to specify data sources, ETL process design, and business logic that connects data sources. Also, we define data warehouse design, solution architecture, and technology selection.

Implementation

In this phase, we set up the infrastructure and develop a full-scale solution. Next, we identify and recommend data analytics or data science possibilities once the data engineering part is done.

Why choose us?

1.

Our first step is understanding and discovering your data sources. From there we pinpoint your problems and define your goals.

2.

We suggest the best way to approach your objectives by deciding on methods and approaches on how to collect and process your data.

3.

You get full support from start to finish with us making sure you fully understand each step.

4.

We aim to prepare your data in the best way possible for further analysis and data science processes.

5.

Our goal is to provide help in designing, operating, and supporting the increasingly complex data environments that power and drive modern data analytics.

6.

Through data engineering, we empower companies to find practical applications of data that drive business success.

Tech stack

Apache Spark logo

Data engineering solutions applications

Admired by

Ryne Braun
Ryne BraunProduct Manager, Dairy.com
Read More
Part of the client’s success is attributed to Digital Poirots consistent, on-the-dot delivery. The team mitigates delays by proactively communicating with subject matter experts. They also provide thorough reports that eliminate the need for lengthy back-and-forths.
Marin Kosović
Marin KosovićLead Data Scientist, Bellabeat
Read More
Digital Poirots leads a precise execution, meeting the team’s requirements. They communicate effectively, establishing a seamless workflow. They were very prompt and precise in response. Their professionalism and expertise were impressive.
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