Data Engineering – NEUROSYS

Data Engineering Services for AI projects

Bring high-profile Data Engineers to your organization that will scale your capabilities when needed.

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NEUROSYS in numbers

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NEUROSYS Data Engineering services

At NEUROSYS we are experienced in developing data platforms on-premise and in the cloud for big Enterprises like Elisa, Banglalink, and Startups like Hepta using the right technologies for the types of data, analytical needs and business processes. We understand the specific requirements of analytic workloads, how they differ from the operational workloads that most information systems are designed for and what are the best technologies available to store and process the data.

Data Platform design

We provide the architectural design for a data platform depending on your required use cases, leaving the design flexible enough to support changing requirements and new use cases in the future.

We design the platform from both the hardware and software viewpoints, considering the performance the underlying hardware can provide and the workloads that the software has to support. The technological landscape is in constant development as new hardware, cloud services and technologies open the doors for performance increases and completely new use-cases.

Data integration

Businesses often collect data in various distinct locations and technologies, such as on-premise relational databases, CRM tools, analytics tools, object storage and so on.

This may work well for operational tasks, but to develop analytics tools and AI models to generate insight from these data, it’s often necessary to integrate said data to a common platform where analytical and operational workloads can be kept separate and the data from various sources used together.

We work with our partners to understand the nature of said data, develop an integration strategy and realize this in either a new architecture or in an existing one.

Big data

The term itself is loosely defined, but quite clear from the perspective of the challenge – handling big data requires different, and far more complex, tools from small data.

At NEUROSYS we’ve had to deal with all kinds of data and have the experience to know when using the more complex toolset is merited and worth the extra cost in development and maintenance.

And if you really need it, we can help you make the right choices and build the system that helps you solve your problems.

NEUROSYS Data Engineering services

Data Engineering Processing Services

Data Ingestion
Data storage
Data processing

Data Ingestion

We use Kafka, Kinesis, Pub/Sub or similar for data ingestion and initial processing.

For simpler workflows like batch processing, we use data pipelining tools such as Apache Airflow and different data source connectors or cloud platform tools such as AWS Glue, Lambda and Data Pipeline.

Data Storage

We use Kafka, Kinesis, Pub/Sub or similar for data ingestion and initial processing.

Data processing

For orchestration Apache Airflow on-premise and AWS lambda with event triggers or GCP Dataflow are our tools of choice.

The processing itself is handled by various tools from Spark for big data to Tensorflow for deep neural networks.

We work mainly in the Python and Linux ecosystems and have extensive experience with relevant tools.

How we work?

Mapping the problem

Working closely with your company, we will identify the key areas where AI can bring the most value. This involves meetings with all stakeholders and developing a roadmap for action together.
Familiarization with existing infrastructure

We analyze the infrastructure that you currently use and make a plan on how to utilize this for the current goals or how to extend this to accommodate the new requirements.
Proposal for deliverables

Based on our meetings and data analysis, we’ll share with you the possible solutions. We will work hand in hand to agree on the desired outcome.
Development in a test environment

Your data is valuable, so the tools processing it must be checked to avoid data corruption or loss. Before deployment, we set up everything in a test environment and ensure everything works end to end.
Deployment to work with live data

When the system has passed all required tests we can deploy it to live environment. Monitoring will, of course, still be set up to notify us about any inconsistencies before they turn into problems.
Maintenance

All systems require maintenance from time to time and data processing can be especially sensitive in this case, as data changes over time along with your organization and your customers. We are happy to support our customers with ensuring the performance of the system..

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Frequently Asked Questions

What does a data architect do?

A data architect is a person responsible for the data architecture principles and the design of systems that manage and process data.

What does a data engineer do?

A data engineer is a specialist proficient in data storage, processing or pipelining technologies or a combination of these. These are the people who implement the components of data architecture, be it storage, processing, or data management systems.

Why is data architecture important?

Good decisions are based on data and those decisions can only be as good as the quality of the data underlying them and as prompt as the system’s performance permits. Good data architecture ensures data integrity and monitoring. The right technological choices and architecture allow for fast queries and scalability, allowing people to get answers and run analyses faster.

Should I use move to the cloud or focus on on-premises solutions?

The answer to this question is specific to a use-case as sometimes using the cloud is cheaper and more efficient while for other use cases on-premises solutions make more sense. It is quite common to use a hybrid solution as well, where some services are in the cloud while others remain on premises. We can help you figure out the optimal solution for your use-case, design it and build it.

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Data Engineering Services for AI projects

NEUROSYS in numbers