Over the last 10 years, the notion has been that to quickly and cost-effectively gain insights from a variety of data sources (e.g. weblogs, clickstreams, events, IoT and other machine-born JSON or semi-structured data), you needed a Hadoop platform. The proposition with Hadoop-based data processing is having the flexibility, capacity and performance to store and analyze an array of data types, in a data lake, within a single repository.
In reality, analyzing data with a Hadoop-based platform is not simple. Hadoop platforms start you with an HDFS file system, or equivalent. You then need to piece together about a half-dozen software packages (minimum) just to provide basic enterprise-level functionality, such as provisioning, security, system management, data protection, database management and the necessary interface to explore and query data.
Despite the efforts of open-source communities to provide tools to improve the capabilities to operate at the highest enterprise-class level, there is the constant need for highly skilled resources–those who can continually support Hadoop to keep it up and running while enabling users to do more than just explore data.
Snowflake, which is built for the cloud and delivered as a service, provides you with a different option for handling JSON and semi-structured data. Just point your data pipelines to Snowflake, land the data in our elastic storage repository and you have instant access to a bottomless data lake and a full-fledged data warehouse. With Snowflake, you can easily load JSON and query the data with relational, robust SQL. You can mix JSON with traditional structured data and data from other sources, all from within the same database. You can also support endless concurrent analytic workloads and work groups against the JSON data in Snowflake. This is all possible without any impact to performance, concurrency or data consistency-virtually any at scale of concurrency, one or 1,000.
As a combined data lake and data warehouse platform, there is much more you can do with Snowflake. Read more about it with our new eBook, Beyond Hadoop: Modern Cloud Data Warehousing.
You can try out Snowflake for free. Sign up today and you’ll receive $400 dollars of free usage credits, plus the capability to create a sandbox or launch a production implementation.
Rethink what you’ve been told
Hadoop is not a prerequisite in order to gain insights from JSON or other machine data.
When you need to store, warehouse and analyze JSON and other machine data to develop insights or to uncover relationships that can drive business decisions, rethink what you’ve been told. You can support all of your structured and semi-structured data warehousing and analytic workload business needs with a single tool–a tool that is built for the cloud and is an ACID-compliant, fully relational SQL environment that millions of SQL users and programmers already know and are accustomed to. And, Hadoop is not required.