Data Engineering Services for AI projects

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

80
AI Projects completed
85%
Project success rate
20
Projects in 20+ countries
80
AI Projects completed
85%
Project success rate
20
Projects in 20+ countries

Trusted by governments, leading telecoms and banks

Data Engineering Services

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.

 

What clients say about us

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
atte

What clients say about us

What clients say about us

Technology stack

Technology stack
Data Ingestion

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

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

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

 

Testimonials

Testimonials

Latest Projects

Latest Projects
elisa logo

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 logo

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 logo

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

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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