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A New Era of Cloud Analytics


Hadoop was regarded as a revolutionary technology that would change data management and completely replace data warehousing. Such a statement is partly accurate but not entirely true since it has not been the case ever since cloud solutions came into the picture. Hadoop mostly flourishes with projects that involve substantial data infrastructure, meaning it was more relevant around a decade ago when most data analysts analysed large data sets, thus needing Hadoop’s tools to complete such tasks. The emergence of cloud computing has continuously threatened Hadoop since it has the flexibility of processing and analysing both small and large sets of data.

Why the Cloud?

Most organizations have shifted from big data architecture to serverless computing, where they have heavily invested in the cloud, which provides flexibility to the organizations while changing to different workloads depending on their needs.  The cloud provides an efficient platform where clients may access big data analytics services cheaply and easily. The following are some of the desirable characteristics that cloud analytics provides:

Snowflake (the perfect cloud solution that offers better analytics services)

Snowflake provides big data analytics solutions through the cloud that makes it easy and cheap for clients to use its cl0ud platform. It contains the following admirable characteristics:

  1. Snowflake uses a Software-As-A-Service platform which ensures that clients don’t need to purchase any hardware but only need to load their data to the platform and query it from there.
  2. It provides a pay-as-you-go billing system which makes it economical for clients to use the services; only paying for the storage and compute resources required.
  3. It has low complexity when deploying data components to the platform, with no management overheads and no statistics and indexes to manage.
  4. Snowflake contains an array of business intelligence and data management tools that have dedicated usable interfaces to ensure that users can analyse data efficiently.
  5. The platform can support large and small data sets ranging from petabytes to kilobytes of data, thus ensuring flexibility depending on the datasets provided by the client.
  6. The platform also has a quick query latency that can run in milliseconds.


Hadoop provides useful tools that can achieve high performance on the big data queried. It is, however, difficult to deploy and manage data while in the platform which has reduced latency and low-quality support. It is also uneconomical to query small data through Hadoop.  Cloud-based solutions like Snowflake provides a more efficient service to querying data compared to Hadoop. The Snowflake platform can query any size of data regardless of whether it is large or small. It provides 24 hr support services to its customers and a usable interface which makes it easy to deploy and manage data through the platform. Cloud analytics is the future for small and big data analytics, and so is Snowflake.

Find out more about all the benefits Snowflake has to offer you and your business. Sign up for a free proof of concept!

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