Data Engineering Services for AI projects|Big Data engineering

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Data Engineering Services

At MindTitan 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 platform engineering

Our experienced data engineers are familiar with the technologies widely used in the cloud and on-premise data architectures.

We can realize a data architecture from scratch or develop additional capabilities to an existing data platform, such as data storage layer developments (data lakes and warehouses), data pipelining (ETL jobs or complex batch processing of data) or analytical components (BI tools and AI model deployment).

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.

Data storage layer design

Different data and different use cases require different storage technologies. Whether it’s structured data that should be kept in a data warehouse in columnar format or unstructured data like images, video and audio, which is better kept in a data lake.

We design the appropriate storage system with fast interconnects that enable the analytics tools to access the data efficiently, providing the required performance.

Data pipeline development

Data pipelines are used for ETL jobs, and batch processing of data in analytics and machine learning workloads.

Good data pipelines are performant, robust and lend themselves well to monitoring and extending when requirements change.

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 MindTitan 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.

What clients say about us

They feel like an extension to our team, rather than just consultants.

I know I can reach out and get an answer to my question the very same day.

Atte Keinänen , Head of Engineering at Fuzu Ltd

What clients say about us

Technology stack

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

For data lakes and unstructured data, we use Object Storage solutions when possible and HDFS when required by downstream processing.

For relational data columnar data formats like ORC or parquet with a query engine like Presto or a managed solution like BigQuery work wonders, but at times PostgreSQL with columnar data storage will do just fine.

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.

Scale up your Data Engineering capabilities with MindTitan

MindTitan’s team members have worked with Big Data helping the biggest Telecom operators like Banglalink and Elisa to scale up when needed

Estimate my project

How we work?

Mapping the problem

What new capabilities are needed, what use cases they must support, what are the performance requirements and technical limitations. This will form the basis for the next steps.

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.

Testimonials

It’s good to know that when we want to take our language models to the next level, we can call on MindTitan’s expertise.

Atte Keinänen

Head of Engineering at Fuzu Ltd.

Read the Case Study

They didn’t need us to adapt to their product, it was the other way around. They listened to what our actual problems were and then built a solution for our actual business problems. That’s the key to most of the issues we have. And most of the projects are so successful thanks to that.

Mailiis Ploomann

Head of Telecom Services

Read the Case Study

Latest Projects

Elisa network data analytics platform

A complete data analytics platform that ingests the network logs of about 300 000 clients into Google Cloud Platform, processes the data for downstream analytics tasks, reporting using BI tools and performs machine learning on the data stream to provide insights into customer satisfaction in the network based on the signals data received.

Technologies used are Apache Airflow, Google Cloud Storage, BigQuery, Tensorflow and Spark.

Banglalink data pipelines

We developed a high-performance data ingestion and processing pipeline that ingested the network data of 35 million users of a large telecommunications company totaling about 5TB of data daily.

This was implemented on a Hadoop cluster distributed across hundreds of nodes and used Apache Airflow for orchestration, Spark for data processing and Apache Hive and HBase for data storage.

Hepta image processing pipeline

A data pipeline processing hundreds of thousands of high-resolution images using machine learning to generate insights.

As machine learning is very resource-intensive and the workload is periodic, scalability was very important for this task both to ensure performance and keep the costs down. This was realized using AWS Batch, S3, and Pytorch.

Frequently asked questions

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

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.

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.

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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Want to learn more about building your data infrastructure?

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