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C2S helps organisations optimise lakehouse data layouts using DLP to improve query performance, reduce unnecessary data processing and control cloud compute costs.
DLP works with existing data platforms and open table formats, allowing organisations to improve analytics and AI workloads without replacing their established data architecture or tools.
DLP is a data-lakehouse optimisation platform that uses multidimensional indexing and automated data-layout optimisation.
It organises data so queries can identify and read relevant information instead of repeatedly processing entire datasets. This can improve performance and make compute-intensive workloads more efficient.
DLP supports open table formats such as Delta Lake, Apache Iceberg and Apache Hudi, along with platforms including Databricks, Apache Spark and Trino.
C2S can support organisations across the DLP adoption and optimisation lifecycle.
Our services include:
Organise tables so queries can avoid irrelevant data and reduce unnecessary processing.
Improve response times for filtering, aggregation and other data-intensive analytical workloads.
Reduce avoidable input/output and compute consumption by making existing queries more efficient.
Continue using established notebooks, BI tools and query platforms while improving the underlying data layout.
Your questions answered
No. DLP complements Databricks and other lakehouse platforms by optimising how data is organised and accessed.
No. DLP supports established open table formats, including Delta Lake, Apache Iceberg and Apache Hudi.
C2S supports organisations across the UK, Ireland and Europe through onsite, offshore and hybrid delivery models.
Improve query performance and cost efficiency without rebuilding your entire data environment.
