As more organizations integrate AI and machine learning, they need to store greater quantities of data in cloud data lakes. However, this data is often costly to store, compute, and transfer. Using less data isn’t an option for cost savings, either, because machine learning relies on extensive libraries of high-quality data. In fact, 70% of data analytics professionals say that data quality is the most important issue organizations face today.
Data cost optimization can reduce data costs without sacrificing quality. Strategies like observability, tiering, and compression work together to keep cloud data lake storage costs as low as possible. We’ll explore these three strategies in detail below.
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Data cost optimization strategies and how they reduce data costs
Data cost optimization involves multiple steps, starting with effective data management, but this is just the first stage of a long-term, continuous process. As long as organizations have data stored in cloud data lakes, data cost optimization strategies can help reduce data costs month after month.
In the table below, we’ll discuss three of the most effective strategies organizations can use right now to reduce data costs.
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Data Cost Optimization Strategies |
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What |
How |
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Data observability |
Data observability is the process of managing data to ensure it’s reliable, available, and high-quality, which prevents poor-quality data from disrupting outcomes. Observability also reduces data costs by ensuring organizations only pay for the most useful data in cloud data lakes. They can delete non-valuable data or move it to archival or other cold storage locations that are less expensive to maintain. To reduce data costs, focus on these data observability strategies:
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Data tiering |
Data tiering prioritizes data based on utility and frequency of required access. It’s an important step in data cost optimization because it keeps cloud data lake storage expenses down.
Use data visibility tools to organize large volumes of data into appropriate tiers. Some tools can move data automatically, based on when the organization last accessed it. |
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Data compression |
Data compression complements data tiering by reducing the number of physical bytes stored, regardless of tier. Compression reduces the physical bit size of data objects, which makes them less costly to store. This process also lowers the cost of data transfers across networks and regions. Generally, the smaller the file is, the less cloud providers charge to transfer it. Additionally, compression speeds up application performance for apps bottlenecked by data lake read throughput. Fewer bits take less time to move. Decompression is also faster than data transfer speeds, resulting in overall faster performance. For best results, focus on two types of data compression:
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However effective, these strategies can be difficult to implement long-term. Large-scale enterprises may find them particularly challenging to maintain, as they often use more data, resources, and instances than small-scale enterprises. Organizations with small IT teams may struggle if engineers lack the time or resources to practice consistent data management.
In both cases, data cost optimization tools can help. Visibility tools assist with data observability and tiering, while compression tools immediately reduce data costs without impacting downstream usage. The best tool – Granica Crunch – combines sophisticated lossless and lossy compression algorithms to improve data lake storage efficiency.
How Granica shrinks cloud data lake costs
Granica Crunch is a data compression service that uses both lossless and lossy compression algorithms to significantly reduce data costs related to cloud data lake storage. The product makes use of:
- Byte-granular, adaptive compression algorithms.
- Cloud-native support for Amazon S3 and Google Cloud Storage data lakes.
- Lossless compression for tabular and text formats such as Parquet, CSV, and JSON.
- Lossy compression for LiDAR point cloud data that eliminates useless noise.
- Continuous scanning to automatically compress new objects as they land in the lake.
- Secure solutions that run entirely within the organization’s VPC (Virtual Private Cloud).
- Elastic, highly available clusters to handle any data volume.
These features work together to reduce cloud data lake storage costs – up to 80% in some cases. Granica Crunch has no upfront costs, either. Users simply pay Granica a small percentage of their total cloud cost savings each month. If there are no savings then there is no bill, making Granica Crunch no-risk to try out. With effective lossless and lossy compression, organizations can slash cloud data lake storage costs and improve data processing performance at the same time.
To reduce data cost, book a demo with our data cost optimization experts today.