Data Engineering Services

Wherever you go, data engineering is key

About 50% of a big data analytics project’s success lies in data engineering. Our data specialists build efficient and scalable data pipelines, making data ready for further analysis and reporting, and provide data engineering consulting services upon request.

Three pillars of data engineering

Data collection

Experts in all things data, we’ll help you collect structured, semi-structured, and unstructured data from a variety of sources — both internal and external. Our data engineers will easily handle quantitative and qualitative data of any scalability and complexity and prepare it for further processing.

Data ingestion

We have the right knowledge and skills to adapt the collected data into the required formats and structures, build, optimize, and audit scalable ETL/ELT data pipelines, as well as deploy data warehouses and data lakes — for more secure and smooth data storage and analysis.

Data preparation

Leverage our expertise around data acquisition, cleansing, conversion, disambiguation, deduplication, and labeling to make your data easy to consume and process for machine learning algorithms. We fully prepare data for in-depth analysis, advanced visualization, and custom reporting.

Key challenges we solve through data engineering

Scalability

We bank on best performance engineering practices, vertical and horizontal scaling, as well as cloud elasticity and serverless to ensure a great speed for data processing — even when the volume of data increases tenfold.

Data consolidation

We know how to aggregate and prioritize data sources as well as build data pipeline schedules. Among our data engineering services is also scenarios configuration — all to enable high-quality analytics in terms of integrity, accuracy, and variety.

Data quality

Our data engineers ensure the outstanding quality of data, regarding accuracy, consistency, validity, uniqueness, and completeness. The optimal choice of data quality tools, real-time data monitoring — we offer this and more.

Pipelines architecting and orchestration

If you need to build an enterprise-grade analytics solution from scratch, we’re here to assist. Our data engineering experts know how to build secure, highly scalable pipelines that will perfectly feed your BI reports and dashboards.

Data collection from enterprise-grade software, databases, search engines, social media, and other sources
Batch and stream data processing — working with formats like text, audio and video files, images, log events, XML, etc.
Data transformation: filtering, grouping, mapping, deduplication, and depersonalization

Data pipeline optimization

Leverage our data engineering competence to notably improve your data reports quality by optimizing your data processing workflows.

Auditing existing pipelines
Adding new data sources and creating new pipelines to respond to new analytics needs
Debugging, error elimination, and pipelines support

Data pipeline migration

We’ll help you update your legacy tools and data processes to optimize the total cost of ownership and overcome scalability issues.

Lift-and-shift approach: existing business logic is implemented via new tools
Migration with enhancement: a newly designed business logic, development of new analytics features
Tool selection consulting services

Data tools and techs we use

Cloud platforms:

AWS, Google Cloud, Azure, Snowflake

Big data ecosystems and tools:

Spark, Hadoop, HDFS, Hive

Streaming:

Spark streaming, Kafka

Databases:

RDBMS, NoSQL, Distributed databases

Languages:

SQL, Python, Scala, Java

Infrastructure:

Terraform, Airflow, Git, CI/CD, Linux

Team up with our data engineers

Our experts are here to solve data challenges of any complexity.

LET’S TALK BUSINESS

Tap into our cross-domain expertise

Data analysis and visualization →

Detailed reporting and custom dashboards
BI tools implementation
BI systems optimization
Advanced ML analytics

Cloud consulting and development →

Cloud TCO assessment and optimization
Cloud tech stack consulting
Detailed cloud roadmap creation
Cloud-native app development

DevOps competence →

Data architecture analysis and audit
Strategy development and implementation
Resource consumption optimization
Detailed analysis reports with actionable recommendations

We address specific data
needs of

Data product startups and software vendors

Empowering existing software with sophisticated data-fueled features to outperform competitors
Augmenting data teams to ensure faster time to market as well as save time and money on staff recruitment and onboarding
Enhancing post-sales activities: integrating your data engineering solutions into your client’s infrastructure, saving you time and money on further in-house product enhancement
Fully covering data engineering stages of big data projects

M&E, adtech, retail, and edtech companies

Developing data analytics solutions from scratch: content/product performance analysis, customer behavior analytics, media performance analytics, and more
Auditing existing data analytics solutions to indicate bugs, errors, and issues that affect reports and dashboards quality
Helping with legacy data tools migration to resolve scalability issues and optimize TCO

Our big data success stories

Big value empowered by big data: 58% CTR boost due to segmented ad campaigns

Oxagile built a big data-based OTT platform monitoring solution capable of processing historical and real-time statistics.

Scalable data architecture. The solution can handle millions of database records and an unlimited number of users.
Quick data processing. The big data collected by the monitoring system is visualized through intuitive dashboards and meaningful reports.
Data-driven insights. It enabled the client’s big data on user behavior and distribution to generate targeted recommendations for meeting the viewer’s needs with relevant video content.

More details

Modernizing outdated DSP data structure Seamless customized data migration→

Cloud migration for trouble-free media asset management Easy management of huge data volumes in one place→

Maximizing cloud video processing efficiency Reduced costs with smart data traffic→

Powerful analytics for a multicloud platform Optimized database to support/handle millions of records→

Reengineering legacy ticketing system A boost in database performance and BI data analysis→

What our clients say

“A super professional and highly dedicated team”

Delivering big data projects for businesses from multiple industries, we are happy to be appreciated for our knowledge and commitment. We strive to bring value to every customer we serve, be they AdTech software vendors or marketing agencies, ensuring a win-win collaboration scenario.

Sharing news and knowledge

Get valuable insights from Oxagile’s recent articles backed by our data engineers’ expertise in big data and data engineering.

Answering the questions you’re wondering about data engineering

What is data engineering?
What are the benefits of data engineering for businesses?
When does a company need a data engineer?
How do companies use data engineering services?
What is the difference between data engineering and data science?

What is data engineering?

It’s all about big data collection and analysis to help businesses resolve the issues through enhancing data pipelines and optimizing them towards usability. Thus, data engineering companies like Oxagile can design solutions for storing, collecting, and analyzing the massive amounts of data produced by your organization. Would you like to meet with our data engineers and find out how we can serve your needs?

Book a short call

What are the benefits of data engineering for businesses?

Organizations investing in data engineering reach a plenty of advantages, including:

Enhanced business efficiency based on data-driven decisions
Increased revenue thanks to effective business predictions
Improved decision-making processes based on the in-depth analysis of performance metrics
Trend-based actions for keeping up with market needs

When does a company need a data engineer?

Imagine how much data your business generates and the value it could bring if it is processed by BI tools. To make the data work for you, you should clean raw data and organize data sets, which is made easier by data engineers.

How do companies use data engineering services?

Our customers often request to replace costly data infrastructures and transform data pipelines into scalable systems to follow one of the goals below:

Identify new business opportunities
Boost and verify decision-making processes

What is the difference between data engineering and data science?

Data engineers are responsible for building and maintaining data pipelines and infrastructures, while data scientists apply data processing algorithms to extract value from data.