Powering Big Data with Next-Gen Cloud Data Platforms

In this 10-page report, you’ll learn about the following:

› The challenges of log data at scale

› How cloud platforms must embrace the strengths and pushes the limits of object storage

› How Hydrolix ingests and transforms real-time data at scale

› How Hydrolix uses a merge service to improve performance and handle late-arriving data

› How Hydrolix uses techniques like parallelism and predicate pushdown for low-latency queries on big data sets

One of the fundamental challenges of big data is figuring out what to do with it: how long to keep it, where it should go, how it should be structured. A cloud data platform should aim to do this with simple elegance—to provide a place for everything, so everything has its place.

But there is another challenge, too—how to pay for all that data. To answer this question, decision makers and engineers are often forced to discard data or keep it in cold storage where it’s not readily accessible. For businesses that are locked into vendors that are too costly, there is no longer a place for everything, and too much data is not in its right place.

To ensure that your business can keep and process all of its data, and to handle the ever-increasing amounts of data you are sure to generate in the coming years, a data platform needs to offer the following features:


  • Long-term data retention in hot storage

  • Real-time streaming and analytics

  • Scalability

  • Cost-effective data at scale

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