Data engineering solutions

Innowise provides modern data engineering solutions that will take your business to a new level. We will create a high-quality infrastructure and optimize your data flow to extract necessary insights and increase your organization's productivity and performance. Our cloud data engineers will transfer your enterprise data in the shortest time. Our company has already delivered more than 1,600+ projects within 19+ years of market experience, so do not hesitate to trust all the difficult work to our professionals.

Need data engineering services?

We would love to create a high-quality data infrastructure for your business.

What is data engineering?

Data engineering operates with large amounts of raw data, builds and maintains companies’ data pipelines to combine the information from multiple resources, store it in a single place, and prepare it for in-depth business analytics. Data engineers convert data into a usable form for data scientists to interpret. When the data is processed correctly, businesses have access to precise analytics that facilitates fact-based decision-making. As a result, companies are able to solve complex business problems and improve the ways they create new products and services with reduced costs.

Data engineering solutions we deliver

Data architecture building

We establish flexible and highly accessible data architecture solutions. The framework combines the information about how data flows in a particular company. A good data architecture shows the right path to achieving your business goals.

Data Lake implementation

Data Lakes manage the storage of large amounts of raw, unprocessed data until it is used by analytics applications. We provide this option, so your business can increase productivity and grow faster without extra effort.

Data warehouse implementation

We create data warehouses that collect all the company information from multiple resources in a single repository that is kept separately from an organization’s operational database. This information is used for analytical insights..

Data migration to the cloud

Cloud migration might be an excruciating process, but it's highly important in modern business. Our cloud data engineers will set up your data lake to move your enterprise data fast and cost-effectively.

Data management and compliance

Data management and compliance are needed to ensure that all data is secured and follows the business rules and government regulations. Our team will make sure that your data is protected the right way.

Data analytics and visualization

These tools help to analyze and process large amounts of information and to present it in a simple way. With our data engineering technologies, your company will have high accessibility to all information that can improve your business.

Data engineering consulting

A professional team of engineers is a key to successful data management. Our data engineers design and manage data to prepare it to be reported for providing better results and decisions backed by data.

DataOps implementation

DataOps practice improves communication, integration, and automatization of data flows between data managers and consumers across the company. We can optimize the DataOps, so your business can deliver relevant and high-quality data to customers.

Build better projects with Microsoft Solutions Partner

The partnership with Microsoft marks us as a trusted collaborator for businesses seeking to leverage AI for their digital transformation efforts.

Through our affiliation, we can offer our clients robust AI solutions and tools, critical for creating smarter applications and managing data effectively. The recognition from Microsoft also positions us as a reliable partner able to meet specific customer demands.

Hire big data engineers

It might be difficult to find qualified and experienced specialists who provide good data engineering services. Therefore, hiring a remote cloud data engineer might be a great option. Our professionals can cooperate online to define the infrastructure and advanced technologies that will collect, improve, and turn your unstructured data into perfectly arranged analytics.

A big data engineer is an expert who works with advanced programs that divide this specialty:

AWS data engineer

AWS data engineer or Amazon data engineer uses Amazon Web Services that participate in data tasks for such brands as Coca-Cola, Netflix, Volkswagen, etc.

Azure data engineer

Azure data engineers operate through the Microsoft Azure platform. The number of projects and products made with this platform includes such companies as Asos, Bosh, PepsiCo, etc.

GCP data engineer

GCP data engineers work with Google Cloud Platform to provide data management solutions for companies. GCP cases list such market giants as Ikea, Spotify, Paypal, Procter & Gamble, etc.

DataOps engineer

DataOps engineers don’t work with data itself, but work with the operation line that is used to build data. They use all the previously mentioned platforms (Azure, GCP, AWS) and others to follow the data flow process.

Looking for help with setting up the cloud data?

We have a whole team of professional data engineers available

Available data engineers for hire

Matthew M.

Senior Data Engineer 4+ years of experience

SUMMARY

Knowledgeable Data Engineer with over 4 years of experience of making data accessible so that clients can use it to evaluate and optimize their performance.

Apache Airflow Apache Hadoop Apache Hive Apache Spark AWS CI\CD

Docker Google Cloud Kafka Kubernetes MySQL PostgreSQL Pyspark RabbitMQ

Request full CV

Andrew D.

Senior Data Engineer 6+ years of experience

SUMMARY

Senior Data Engineer with 6+ years of experience in designing, maintaining, and optimizing data infrastructure for data collection, management, and transformation.

Apache Airflow Apache Hadoop Apache Hive Apache Spark AWS Azure

CI\CD Docker Kafka Kubernetes MySQL PostgreSQL Pyspark RabbitMQ Terraform

Request full CV

Phil K.

Senior Data Engineer 5+ years experience

SUMMARY

Senior Data Engineer with experience in working on a large number of projects with complex architecture, various technologies, and tight deadlines, and providing assistance in all project phases.

Apache Airflow Apache Hadoop Apache Spark AWS Azure Google Cloud Platform

MongoDB MySQL PostgreSQL Pyspark Python Redis

Request full CV

Stephen K.

Senior Data Engineer 5+ years experience

SUMMARY

Experienced Data Engineer who streamlines data science workflows to solve business problems, building and maintaining the infrastructure to improve processes.

Apache Airflow Apache Hadoop Apache Hive Apache Spark AWS Google Cloud Platform

Kafka MySQL Numpy Pandas PostgreSQL Pyspark Python SAS Analytics Pro SAS BI

Request full CV

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The value of our data engineering services

Improve data quality

Our data engineers help businesses to collect data from several sources and validate information before using it in analytical systems. It mitigates the risk of making misinformed decisions based on irrelevant data.

Make the most of big data

Innowise uses advanced system algorithms that manage large amounts of data and combines the data from different sources into a single repository for further processing.

Increase productivity

We provide complex services that boost your business’s data-driven operations, complete them and prepare for accurate analysis in the shortest time.

Reduce costs

Our data engineering services are highly cost-efficient. As experts in big data technologies, data engineers find the most efficient data architecture solutions and pipelines for individual businesses needs.

Data engineering significantly improves the quality of the company’s data, helps to store it safely, and prepares it for interpretation by a team fast. Expand your company, not worrying about the storage of big data and the difficulty of its processing. Talk to us.

“Big data arrays are often unstructured and hard to read. Our data engineers make the process of maintaining them as easy as possible by building data architectures and pipelines. They also implement data lakes to store and utilize large volumes of raw data and data warehouses for already structured and processed data arrays. After the data cluster gets managed by data engineers, we can use it for our needs and make informed decisions. These decisions usually result in process optimization or increase in such metrics as sales or conversion.”

Philip Tikhanovich

Head of Big Data and AI

Our data engineering tech stack

Our data engineers are professionals who can solve any data problems. They quickly navigate through advanced technologies, creating the best data engineering solutions in the market. The engineers operate with such programs as Amazon Web Services (AWS), Google Cloud Platform (GCP), Azure, and Apache. Many data specialists use python for data engineering.

S3
Glue
EMR
Lambda
Athena
SQS
CloudWatch
EC2
Transfer Family

EFS
EBS
S3 Glacier
Kinesis
QuickSight
API Gateway, etc

Data Lake
Data Factory
DataBricks
Functions
Blob Storage
Data Explorer
Data Catalog
Data Share
Power BI, etc

DataProc
DataFlow
Cloud Storage
FileStore
Cloud Functions
DataPrep
Pub/Sub
KMS
DataStore
Compute Engine, etc.

Airflow
Hadoop
Spark
Hive
Cassandra
Beam
Kafka
HBase
NiFi
Flink
Superset
Presto, etc.

Power BI
Tableau
Google Data Studio
Looker
QuickSight
QlikView
Qlik Sense, etc.

TensorFlow
Keras
PyTorch
Theano
SciPy
Caffe
Sklearn
OpenCV, etc.

Pandas
NumPy
Matplotlib
Seaborn
Plotly, etc.

dbt
TimeXtender
Azkaban
Cloudera
Segment, etc.

Why should you choose Innowise as a data engineering company?

During our 19+ years on the market, we have delivered 1,600+ projects for 700+ satisfied customers. If your company has data processing or data storage issues, let our team help you!

The Innowise guarantees the safety of your data. We use advanced encryption systems that secure your files and back up all types of data. We keep our work transparent and always inform the customer about the steps of data engineering processes.

We understand how important the organization’s information can be, so we do our best to save your time. Our data engineers will structure and optimize a company’s data to extract and deliver large amounts of business insights in the shortest time possible and with minimal costs.

Our clients on working with Innowise

Innowise has a strong understanding of the task at hand, enabling them to offer the most suitable development approach. The team exceeded our expectations and is responsive when we request changes.

Ilya Radniany CEO Duck.design

We are delighted with Innowise's commitment to delivering quality work and solving issues quickly. They lead an engaged approach to understanding the team's needs and accomplishing their goals.

Or Iny CEO Zero Beta

Innowise is one of the best partners the client works with. They always fulfill our requests by providing developers that meet the client’s specific language and experience-level requirements.

Polina Shuruba Partner Manager XPRT MINDS

Our data engineering process

We approach every customer individually! We cooperate to define technologies, infrastructure, and advanced technologies that solve specific business challenges and match your architecture.

Requirements analysis

At the very first step, we determine users' detailed needs and expectations for a new or modified product. It is a plan for all the subsequent data-related processes.

Data architecture design

We establish a framework that shows the sources of information, and how this information is being transported, secured, and stored. Data architecture manages the data strategy.

Data ingestion

We transport the data to a storage medium or import it for immediate use.

Data cleaning

Before the data makes it to the pipeline, it needs to be cleaned first. We correct or remove all the irrelevant and incorrect parts of the records.

Data Lake building

We build Data Lakes to store raw, structured, and unstructured data files in one repository with minimal costs. Data Lakes might be created through such programs as Hadoop, GCS, or Azure. That includes such complicated operations as data engineering with python.

ETL/ELT pipelines implementation

After preparing the stored data, the ETL engineer starts the data processing operations. It is the most critical act in the data pipeline because it turns raw data into relevant information.

Data modelling

At this step, we explore and visualize the data-oriented structures. The goal is to represent the relationships within the data and illustrate the types of this data and how they can be grouped.

Quality assurance

Before sending the data any further, it needs to be tested and get quality-approved. Our specialists create test cases for verification and validation of all elements of data architecture.

Automation and deployment

This is one of the most important steps in the whole process. Our team creates the DevOps strategy that automates the data pipeline. This process saves a lot of time, money, and effort spent on pipeline management.

Want to safely transport your organization's data?

Our engineers are always at your service!

Data engineering solutions: related cases

FAQ about data engineering

What is the difference between data engineering and data science?

Data engineering focuses on building and transforming the data into an accessible format. Data science analyzes the data and provides visualizations to explain its results. Data science and engineering are interconnected because the data engineer’s job is to send transformed data to a data scientist.

Do I need data engineering?

Data engineering is important in any data-driven business because it allows companies to optimize and utilize large amounts of data for usability. It is cost-efficient, improves data quality, increases productivity, and manages to save a lot of time.

What is a data pipeline?

A data pipeline (or data connector) is a set of data processing steps needed to automate the movement and transformation of raw data between a source system and a target location. Data pipelines give team members the prepared data that they can work with.

Why is data engineering so important?

Data is the foundation of any business and needs to always be accessible and reliable for the team. Data engineering makes the data convenient and transparent for analysis in the shortest time. A great advantage is that massive amounts of data can be stored and managed, there are almost no limits.

What is DataOps?

DataOps is a practice that improves communication, integration, and automatization of data flows between data managers and consumers across the company. With this feature, organizations can deliver relevant and high-quality data to customers.

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What happens next?

Once we’ve received and processed your request, we’ll get back to you to detail your project needs and sign an NDA to ensure confidentiality.

After examining your wants, needs, and expectations, our team will devise a project proposal with the scope of work, team size, time, and cost estimates.

We’ll arrange a meeting with you to discuss the offer and nail down the details.

Finally, we’ll sign a contract and start working on your project right away.