Data Engineering Services

Providing complete assistance to our clients with data engineering services. We want them to fully understand their data-related challenges.

Build automated advanced data pipelines with Addepto team!

Our Data Engineering professionals can assist you with using the data you collect and developing innovative data pipelines utilizing cutting-edge technologies and platforms.

We are experienced in both Cloud technologies and on-premises solutions. We have hands-on expertise with AWS, GCP, and Azure data engineering.

– Edwin Lisowski, Chief Technology Officer at Addepto

What is Data Engineering?


Data engineering is the practice of designing and building systems for data collection, storage, and analysis. This is an extensive field of application in almost all industries. Data engineering includes many data science majors.

In addition to providing access to data, data engineers create an analysis of raw data to provide predictive models and show short-and long-term trends.

\Without data engineering, it would be difficult to make sense of the huge amounts of data available to companies.

Who is a Data Engineer?


Data engineers are familiar with a variety of programming languages used in data science. Data engineers build data pipelines that connect data from one system to another.

They are also in charge of transforming data from one format to another, allowing the data scientist to obtain data from other systems for analysis.

What Is Data Engineering Services?


The Addepto Data engineering services will help your business in advancing to the next level of data usage, data management, and data automation.

Our specialist team assists worldwide enterprises such as JABIL, SITA, and J2 Global in the development of data processing pipelines.

We are working with our customers to extract important business information, manage data, and ensure the highest level of data quality and availability.

Our project strategy and big data engineering services were created to help companies make better decisions. You may focus on insight extraction thanks to automated advanced data pipelines.

The benefits of Data Engineering Services

We can guide you through the entire process, working closely with your company, its challenges, strategy, and the questions you have. Wherever you are, we can offer a complete, end-to-end solution. Here are some of the ways it can help your business:

Modern Data Pipelines


Designing, constructing, and implementing end-to-end automated data pipelines of production quality.

The Addepto data engineering consulting team has strong experience in the implementation of automated data pipelines, both on-premises and in the cloud.

Data Preparation and ETL/ELT

Data preparation, processing, and ETL/ELT (extract, transform (load), load (transform)) help in the processing, transformation, and loading of data into the required data model for business reporting and advanced analytics.

Our Data Engineering team has developed such pipelines for many business departments such as Finance, Sales, Supply Chain, and others.

Data Lake Implementation


Data Lakes are the most powerful and creative option for cost-effective data storage and quick processing. Adoption of Data Lakes in your company may support you in expanding the business data architecture.

Addepto has used Data Lake solutions to solve a variety of client business challenges such as Product Traceability, Customer Data Platforms, IoT data reporting, and others.

Cloud Data Architecture


Today, it is essential to build and design flexible and highly accessible business data architectures.

Our Data Architects can help your business get to the next level in terms of data analytics foundation by combining experience from several large enterprises. Try our Big Data Engineering Services!

Addepto team is at your service!


During our 3+ years on the market, we have delivered 100+ projects for 30+ clients in different industries.

We approach every customer very individually and determine a specific project approach.

We cooperate to define technologies, infrastructure, and advanced technologies that will solve specific business challenges and match your architecture.

Relevant Case Studies

Product Traceability System for a big manufacturing company


We helped JABIL a big electronic manufacturing company to build a complex Data Lake system based on AWS for Product Traceability.

Addepto Data Architects and data engineering experts have designed and implemented an end-to-end scalable system for fast analytical reporting and data storage.


Customer Data Platform implementation


Addepto team has supported the Custimy team with their data lake and analytics journey.

Our data engineering team has created a tailor-made data transformation layer for both structured data and Digital Marketing data sources, combined together in a single and unified cloud data warehouse.


Our Data Engineering Services Process

Addepto is an experienced Data Engineering company. We help companies all over the world make the most of the data they process every day.

Below you could find our workflow of implementation data engineering solutions and pipelines:

1. Understanding business needs and technical requirements

Firstly, our data engineering team carries out the workshops and discovery calls with potential end-users. Then, we get all the necessary information from the technical departments.

2. Analysis of existing and future data sources

At this stage, it is essential to go through current data sources to maximize the value of data. You should identify multiple data sources from which structured and unstructured data may be collected. During this step, our experts will prioritize and assess them.

3. Building and implementing a Data Lake

Data Lakes are the most cost-effective alternatives for storing data. A data lake is a data repository system that stores raw and processed structured and unstructured data files. A system like stores flat, source, transformed, or raw files.

Data Lakes could be established or accessed using specific tools such as Hadoop, S3, GCS, or Azure Data Lake on-premises or in the cloud.

4. Designing and implementing Data Pipelines

After selecting data sources and storage, it is time to begin developing data processing jobs.

These are the most critical activities in the data pipeline because they turn data into relevant information and generate unified data models.

5. Automation and deployment

The next step is one of the most important parts in data development consulting – DevOps. Our team develops the right DevOps strategy to deploy and automate the data pipeline.

This strategy plays an important role as it helps to save a lot of time spent, as well as take care of the management and deployment of the pipeline.

6. Testing

Testing, measuring, and learning — are important at the last stage of the Data Engineering Consulting Process. DevOps automation is vital at this moment.

Let’s discuss a data engineering solution for your business

Our Data Engineering Tools and Technologies

The Addepto team uses the most advanced tools and technology on the market. To supply stable and high-quality software, we partner with the largest cloud solution providers (AWS, Azure, and GCP).

Our data engineering team is also deeply committed to the open-source community and technology, so our clients don’t have to pay extra for some of the most popular data engineering software.

How big tech companies use data engineering


Many e-commerce giants use the power of data to create value for their businesses. Specific data allows you to attract potential customers and thereby significantly increase business profits.

Amazon personalizes every interaction by using a large amount of client data.

Data is being used by the company to optimize pricing, advertising, the supply chain, and even to decrease fraud.

Nordstrom’s data engineers have developed a system for monitoring customer habits and behavior using Wi-Fi.

The data obtained allowed the company to study the purchasing trends of its customers, which resulted in the optimization of personalized data and overall improved customer service.

Discover the rest of our data-related services. Find out how Addepto solutions can change your business.

Addepto customers about our Data Engineering Services


Our customers come to Addepto with complicated data challenges, and we work together to solve them. We are all working together to achieve business strategic goals and put the best data engineering services in place.

This guarantees that Addepto works as an extension of our customers’ teams, maintaining a close connection with their requirements. We are ranked among the top Data Engineering Services companies on Clutch.

The modules they created provide accurate and detailed reporting that allows an in-depth analysis of all KPI’s and much more.

Addepto’s project management approach was streamlined and efficient, which produced top-notch results.

Jacek Szlendak

CEO, Perfect Gym Solutions S.A.

Work was progressing well and both teams were in constant communication on Slack.

Project management was conducted by the team leader, most of the issues were consulted at online meetings.

Patryk Kozak

Lead Backend Developer, Gamesture Sp z o. o.


Finished Projects



Data Engineers



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Industries served

Read Our Blog


Check out our blog and make sure you are keeping up with the last trends in your industry

Data Science vs. Data Engineering

What are the main tools and programming languages used in data engineering and data science?

Find out the main difference between these two fields.

Who is a Data Engineer?

The profession of Data Engineer was ranked as the fastest-growing tech job in 2019!

With the number of open positions up 50% compared to last year.

Data Lake Architecture

In this article, you can learn more about Data Lake Architecture.

Learn what are the key factors you should keep in mind when planning Data Lake Architecture.


What is the difference between Data Engineering and Data Science?

Data science is an interdisciplinary field that uses methods and techniques from statistics, applied science, and computer science to analyze organized and unstructured data to provide useful insights and information.

Data engineering is responsible for creating a pipeline or procedure to transport data from one instance to another.

Do I need Data Engineering?

We are surrounded by data. This resource may be used for a variety of purposes, including customer service, market research, and, of course, sales. Developing sophisticated data systems for businesses is quickly becoming necessary.

You should hire data engineering consulting experts to organize your system and use the data to improve your business performance.

What is a Data Pipeline?

A data pipeline is a sequence of data processes that extract, process, and load data from one system to another.

Data pipelines are classified into two types: batch and real-time.

What is the future of Data Engineering?

The following four areas were highlighted as technological shifts in data engineering of the future:

+ Increased connectivity between data sources and the data warehouse.
+ Self-service analytics with intelligent tools made possible by data engineering
+ Automation of Data Science functions
+ Hybrid data architectures spanning on-premises and cloud environments

What does a Data Engineer do?

Data engineers are responsible for the design, development, and maintenance of the data platform, which includes the data infrastructure, data processing applications, data storage, and data pipelines.

In a large company, data engineers are usually divided into teams that focus on different parts of the data platform: Data warehouse & pipelines, Data infrastructure, Data applications.

Why is Data Engineering so important?

To establish a genuinely effective analytics program, companies must intentionally invest in developing their data engineering expertise.

This includes building a solid basis for data management-identifying gaps and quality concerns while improving data collecting.

Companies that actively invest in engineering professionals will get the most out of data in the coming years.

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