Businesses dealing with growing data face two main issues, rising storage costs and declining query performance. As data grows, cluttered storage leads to high cloud bills and slow queries delay analytics and decision making. Traditional compression methods can’t get the balance right between cost and performance and leave businesses with high overhead costs. By using Snowflake’s advanced compression techniques, Dataplatr as Snowflake partners help organisations optimise storage, reduce costs and improve query performance without compromising data integrity.

Removing Storage Bloat with Smart Compression

Many businesses unknowingly store redundant, bloated or poorly formatted data that takes up too much space. Traditional compression methods require manual tuning or result in data loss. Dataplatr, as a trusted Snowflake integration partner, helps businesses implement columnar storage and adaptive compression in Snowflake. Unlike generic compression, Snowflake dynamically selects the most efficient compression method based on data type and structure. With Dataplatr’s expertise in data partitioning, deduplication and automated clean up processes, businesses can reduce storage footprint while keeping high data accuracy.

Faster Queries without Increasing Compute Cost

Compressing data shouldn’t come at the cost of performance. Many organisations experience slow queries and increased compute time after applying generic compression. This happens when data needs to be decompressed for every query and that’s unnecessary processing overhead. With its Snowflake partnership, Dataplatr ensures businesses use Snowflake’s hybrid columnar compression to store data in a format that’s optimised for fast retrieval. By minimising unnecessary decompression and improving data indexing, Dataplatr helps organisations run high performance queries with reduced compute cost. So, analysts and data teams get results faster without needing to scale compute resources unnecessarily.

Implementing a Cost-Effective Scalable Storage Model

Storing large datasets without optimisation will cause long term scalability issues. Organisations that don’t implement tiered storage models end up paying for frequently accessed and archival data at the same rate and that’s avoidable costs. As Snowflake partner Dataplatr categorise and store data efficiently by segmenting data into hot (frequent queries), warm (periodic access) and cold (archival) tiers, businesses can make sure they’re only paying for the storage they really need and have access when they need it.

Data storage shouldn’t be a choice between cost and performance. Dataplatr, along with Snowflake partners, gives businesses the tools to remove storage bloat, speed up queries and cut cloud costs through advanced compression.