3:40 pm · Big data
Illustration
Big data services that turn scattered data into answers
Data lakehouses, real-time pipelines, governance and dashboards on Databricks, Snowflake, BigQuery and more, so large, scattered data gives clear answers.
The short answer
Big data services bring all your data, from every system and in every format, into one platform where it can be queried in seconds and analysed as it arrives. We build data lakehouses, real-time pipelines, machine learning and dashboards on cloud platforms, and we set up the governance that keeps it all secure and compliant.
How it works
We assess your data.
We audit your current infrastructure, sources and use cases, then design an architecture that grows with you and supports real-time analytics. This usually takes two to three weeks.
We build the platform.
We set up a data lake or lakehouse on Databricks, Snowflake or a cloud-native setup, with pipelines that bring in every type of data. That usually takes four to six weeks.
We add analytics and machine learning.
We add automated processing, machine learning models and real-time analytics, plus self-service reports that stay inside your governance rules. Expect three to five weeks.
We build the dashboards.
You get interactive dashboards and reports, with alerts on the numbers that matter. This usually takes two to three weeks.
We tune and scale it.
We speed up queries, keep costs in check and let capacity follow your usage, with ongoing support.
What you get
Screens are mock-ups
A data architecture that removes silos and supports real-time analytics
A lake or lakehouse on Databricks, Snowflake or a custom cloud setup
ETL and ELT pipelines with scheduling and monitoring
Machine learning models and AutoML for patterns and predictions
Real-time analytics with Kafka and Spark
Dashboards in Tableau, Power BI or Looker
A governance framework for GDPR and CCPA, data quality and lineage
Ongoing query tuning, cost controls and scaling
What a modern platform changes
One home for all data.
Structured tables and unstructured files sit on one platform, with fewer fragile pipelines between them.
Answers as data arrives.
Streaming analytics lets you act the moment something happens instead of waiting for the nightly report.
AI built in.
Machine learning finds patterns and trends that people might miss.
Scale on demand.
Cloud capacity grows and shrinks with your workload on AWS, Azure or Google Cloud.
In numbers
- 500+projects completed
- 98%client success rate
- 24/7expert support
The market
- 463 EBExabytes of data a day worldwide by 2025
- 23xFaster decision-making
- 65%Use AI for analytics
- 60%Cost reduction potential
Success story
DataMax
300% faster insights with data lakehouse architecture
We moved DataMax from a legacy data warehouse to a lakehouse on Databricks with real-time processing. Queries ran 300% faster, costs fell 60%, and real-time customer analytics became possible.
Results
- 300%Faster query performance
- 60%Infrastructure cost reduction
- 95%Data pipeline reliability
- 24/7Real-time processing
Tools we work with
Databricks, Snowflake, Amazon Redshift, Google BigQuery and Azure Synapse for storage and compute, Apache Spark and Kafka for processing, Apache Airflow for orchestration, dbt for transformation, Tableau, Power BI, Looker and Qlik for visualisation, Elasticsearch for search, and MongoDB and Cassandra for NoSQL data.
Where it fits
A data platform fits when data is spread across many systems, grows quickly, or arrives as a constant stream from apps, sensors or transactions. To forecast from that data, see predictive analytics. For smaller datasets and a first dashboard, start with data and machine learning.
Price
We quote every project in writing after a free consultation, and we agree the timeline in the same quote. Cloud and platform costs are paid separately. You'll find full details on the pricing page.
FAQ
What is a data lakehouse?
It's one place that stores all your data, raw files and tidy tables alike, and lets you run fast reports and machine learning on it. It takes the place of the usual mix of a data lake, a warehouse and the pipelines between them.
Do we have big data, or just a lot of spreadsheets?
A proper data platform helps if your reports take hours to build, your data lives in many systems, or events arrive faster than a spreadsheet can take them. If none of that sounds like you, our data and machine learning service may be the better start, and we'll tell you so.
Which cloud do you use?
Where we can, the one you already use, whether that's AWS, Microsoft Azure or Google Cloud. Platforms like Databricks and Snowflake run on all three.
How do you keep the data compliant?
We set up governance from the start. That covers who can see what, where each number came from, how long data is kept, and the controls GDPR and CCPA require.
How much does a data platform cost?
We quote every project in writing after a free consultation, based on your data sources, volumes and the reports you need. Cloud and platform costs are paid separately, and we put cost controls in place.
Related
- 2:40 pmAPI integrationAPI integration and middleware that keeps your systems in sync
- 10:20 amAI integrationAI integration for the systems you already run
- 11:00 amNo-code and internal toolsTrackers, dashboards, approval flows and client portals, built fast on no-code and low-code platforms so your team can edit them itself.
Pictures are illustrations
Next step
Tell us where your data lives today and what you wish you could see, and we'll sketch the platform with you.
Book a free consultation
