Scale your business with Data Engineering Solutions

Create a data architecture and build data pipelines as the foundation of data-driven business

Data Engineering - the very foundation of data-driven insights

The role of data analytics is more important than ever. The volume, variety, and velocity of data being collected worldwide has exploded in recent years and smart businesses are using it to gain actionable insights, make informed decisions, and gain competitive advantage. In its report ‘Worldwide IT Industry 2018 Predictions’, IDC predicts that 90% of large enterprises will generate revenue from data as a service by 2020. This represents a massive increase from almost 50% in 2017.

Netflix creates Metacat to combine diverse data sources

Netflix creates Metacat to combine diverse data sources.

Netflix derive their market-leading ability to offer personalized show and movie recommendations to their users from analysis of big data and predictive algorithms. They created Metacat to bring their diverse set of data sources together and to ensure their data platform can interoperate as one ‘single’ data warehouse.

Netflix - Machine Learning
  • We think the combined effect of personalization and recommendations save us more than $1B per year.
    Carlos A. Gomez-Uribe

    Carlos A. Gomez-Uribe

    Former VP of Product Innovation at Netflix Inc.

Data Engineering - the foundation of a solid data-driven strategy

As the foundation of a solid data-driven strategy, data engineering generates significant business value. Here are some of the ways your business can benefit from it:
  • Increase productivity. Data engineers add business value by anticipating what a data scientist needs, and providing them with usable data.
  • Improve data quality. With data engineering, businesses now have the ability to gather data from a large number of sources, clean it, and validate it before feeding it into analytical systems.
  • Reduce costs. As experts in big data technologies, data engineers are best placed to identify the most efficient and effective data architecture and processing pipelines for individual businesses’ needs.
  • Harness the power of big data. Although data engineering can be valuable when applied to any size data set, it really comes into its own when used in big data analytics.

Starting a Data Engineering project is not easy

Every organization is unique in its data engineering requirements, which is why it's important to tailor-make intelligent solutions that are capable of scaling with your business.

Any company which depends on high quality information for decision-making can benefit from data engineering and its subsequent application in data science.

Our process

Over the years, our team has tested and implemented a transparent and efficient workflow for Data Engineering projects. The process helps our customers receive more reproducible results faster and in a more flexible way. Our workflow focuses on four stages:
  • Establish a solid data architecture. Aligning your organization’s data with your business strategies is key to success. We help you establish a sound data architecture which achieves this by providing guidance on how data should be collected, integrated, validated, stored, and delivered to the right people.
  • Implement a scalable data platform. Designed to capture, store, and process large volumes of data, a data platform consists of components such as big data storage, databases and file systems, business intelligence, along with management and administrative tools.
  • Create processing pipelines. A data pipeline is essentially a set of tools and processes that are used to combine data from a variety of sources into a unified interface that allows users to produce analytics, statistics, and visualizations.
  • Create long-term strategy. A data-driven approach can help you to create long-term plans and become more dynamic, agile, and profitable. You will be able to create sustainable solutions that can handle the accelerating rate at which new data is being generated.

What is Data Engineering?

In the modern era, data is being generated and stored at an unprecedented rate. In the last two years alone, 90% of the world’s data was created, and the pace is only set to accelerate.

The Internet of Things, social media, web services, mobile devices, transaction data, and databases are among the sources responsible for producing massive amounts of structured and unstructured information, known as big data.

Data science applications are allowing organizations to use big data to take a data-driven approach to solving complex business problems, allowing them to reduce operational costs, create new products and services, and identify new sources of revenue. To do this successfully, they must have access to the right data, in the right format, at the right time.

In most organizations, however, data sets are stored in various formats and rely on different technologies. This is where data engineering provides the solution. While data scientists are concerned with producing insights from a set of data, data engineers focus on getting that data production-ready.

To make data both clear and actionable, it must be cleaned, validated, and prepared for whatever the data scientist is trying to achieve, and allow queries to be run against it. This often means taking a disorganized or unrefined source of data, and converting it into something usable.

Data engineers are also responsible for building and maintaining an organization’s data pipeline. This incorporates everything from gathering the necessary data, processing it, storing it, and enabling access to the end user, whilst taking account of the various technologies and frameworks involved.

Who can benefit from Data Engineering?

A growing number of companies, both large and small, are capturing their data and taking advantage of the insights stored within it. Rapid technological advances have made big data analytics more widely accessible, meaning that any organization which depends on high quality information for decision-making can benefit from data engineering and its subsequent application in data science.

A data-driven approach can help your business become more dynamic, agile, and profitable. From enhancing customer experience with a recommendation engine, to predicting future demand, to detecting anomalies and preventing fraud, the possibilities are endless.

Although becoming mainstream in many areas, data engineering and data science have revolutionized certain industries. In healthcare, organizations are using data to recommend treatment options and make lifesaving diagnoses. The financial services industry is using machine learning to identify and reduce fraudulent transactions, along with advances in anti-money laundering, credit risk management, and regulatory compliance. And in manufacturing, artificial intelligence is being used to increase the efficiency of operations and reduce costs.

World-class specialists at your disposal

Our developers are open source contributors with more than 220 repositories on GitHub.
We constantly invest in developing new technologies and testing various solutions in our R&D department, sharing our experience both on expert blogs and at various tech conferences such as IGARSS, AAIA and MICCAI.

Our team has worked on different engagements, including many end-to-end projects.

Data Annotation Platform with Libra

Delegate annotating to the users.

Data Annotation Platform (DAP) is an application that delegates annotating to the users, who in turn can earn a little bit of money for their work. This can tremendously help data scientists and allow them to prototype Machine Learning applications much quicker.

Read case study
libra app

Our partners about the cooperation with Netguru

  • My experience working with Netguru has been excellent. Outstanding software teams are resilient, and our developers at Netguru have certainly proven to be that. Our Netguru friends have become as close to team members as possible, and I am grateful for the care and excellence they have provided.
    Gerardo Bonilla

    Gerardo Bonilla

    Product Manager at Moonfare
  • Netguru has been the best agency we've worked with so far. Your team understands Kelle and is able to design new skills, features, and interactions within our model, with a great focus on speed to market.
    Adi Pavlovic Keller Williams

    Adi Pavlovic

    Director of Innovation at KW
  • Working with Netguru has been a fantastic experience. We received a lot of support in terms of thinking about how we track metrics, how we design this properly, and how we build the architecture. We are extremely grateful for making our platform what it is today.

    Manon Roux

    Founder at Countr

  • 15+

    Years on the market
  • 400+

    People on Board
  • 2500+

    Projects Delivered
  • 73

    Our Current NPS Score

Delivered by Netguru

We are actively boosting our international footprint across various industries such as banking, healthcare, real estate, e-commerce, travel, and more. We deliver products to such brands as solarisBank, IKEA, PAYBACK, DAMAC, Merck, Volkswagen, Babbel, Santander, Keller Williams, and Hive.

  • $5M

    Granted in funding for Shine. Self-care mobile app that lets users practice gratitude
  • $28M

    Granted in funding for Moonfare. Investment platform that enable to invest in private equity funds
  • $20M

    Granted in funding for Finiata. Data-driven SME lending platform provider
  • $47M

    Granted in funding for Tourlane. Lead generation tool that helps travelers to make bookings

Start your project with us or 
take existing one to next level

Let's work together
How Web Development Company Can Help You Kickstart Your Business

Looking for other services?

Check out the other services that we have in our range. We deliver high-quality products on time. Hassle-free.
See other services