Big Data consulting tailored to your business needs.

We design Big Data platforms. We realise the full potential of data. Data is about people - for people. We can simply say - Datumo is dedicated to helping people understand each other.

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Big data experts on board
Successfully delivered long-term projects
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We are the best in

Data Platform Creation

Data Platform Creation

  • data platform designing and developing on Google Cloud, Snowflake and Azure
  • optimizing platform cost and efficiency
  • creating data catalogs with data lineage information
  • architecting Customer Data Platforms (CDP) on Google Cloud, Snowflake and Azure
  • constructing data lakes on BigQuery and Snowflake with built-in data sharing mechanisms
Data Platform Migration

Data Platform Migration

  • planning and implementing data platform migrations
  • developing tools for seamless migrations
  • ensuring data synchronization during transitions
  • enabling transitions to modern services like Snowflake and BigQuery
Data Platform Modernization

Data Platform Modernization

  • deploying distributed schedulers, such as Airflow to streamline and automate your data workflows
  • introducing GitOps and DataOps approaches to enhance platform management
  • implementing DevOps practices and automating the creation of developer environments using Terraform
  • optimizing data pipelines and workflows for scalability and reliability
  • providing training and knowledge transfer to empower your team in modern data platform practices
Data Pipelines Engineering

Data Pipelines Engineering

  • designing and automating ETL workflows using technologies like Spark, Airflow, Databricks, Snowflake, DBT, and BigQuery
  • centralizing and automating month-end closing and reconciliation processes, including data scheduling and migration
  • designing ETL processes with a focus on compliance and data quality
  • validating KPIs and ensuring the accuracy of business metrics
  • maintaining and optimizing Databricks environments, automating processes with tools like Databricks Workflow, Azure Data Factory, and Control-M
Realtime System Creation

Realtime System Creation

  • architecting and building real-time data streaming systems, processing data in an event-based fashion
  • specializing in creating real-time pipelines using technologies like Spark, Snowflake, Event Hub, Kafka, Python, Druid, and Spark Streaming
  • optimizing and scaling real-time data processing infrastructure as the workload grows
  • developing mechanisms for real-time fault detection and response
  • deploying custom modules like Azure IoT Edge, Kafka Connect, and Kafka Streams for tailored solutions
Observability & FinOps

Observability & FinOps

  • conducting cost analysis and providing cost optimization recommendations for cloud (GCP, Azure), Snowflake and Databricks environments
  • building monitoring solutions to track resource utilization, performance, and cost-efficiency
  • offering regular reporting on resource utilization and cost management to ensure transparency
  • setting up automated alerting systems to detect anomalies and cost overruns in real-time
  • fine-tunning Spark jobs for improved efficiency
AI Model Deployment & Maintenance

AI Model Deployment & Maintenance

  • specializing in deploying and maintaining AI models to optimize various business processes
  • developing customized AI models tailored to your specific needs and goals
  • continuously monitoring and fine-tunning AI models to ensure accuracy and performance
  • providing ongoing support and maintenance to keep AI systems up and running smoothly
We are trusted by:

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Knowledge Zone

Get to know us, discover our interests, projects and training courses.

Datumo Camp 2024: Strengthening Team Bonds

In this blog, you will discover how we spent our time this year, exploring the benefits of a weekend company getaway.
Employer Branding Specialist
Joanna Krzysztofowicz
Header of Spark 3 AQE and caching

How to enable AQE partitions pruning on a cached Spark dataset

In this article, we delve deep into Spark 3's AQE framework, focusing on the coalesce and caching mechanisms of its shuffle partitions.
Senior Big Data Developer
Marcin Szustak

Spark danger: pivot is an action!

When delving into crafting a new and efficient Spark job, or optimising an existing one, multiple implementation and design choices may have a significant impact on the job’s performance. One of the most prominent aspects influencing a Spark job’s efficiency is the fundamental difference between actions and transformations.
Data Engineer
Michał Możdżonek
Data Engineer
Sebastian Skiba

Make BIG data breakthrough!

Send us your inquiry via the contact form. We will contact you and together we will discuss the proposed actions for your data.
+48 789 566 177
Dziekońskiego 1 street,
00-728 Warsaw
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