Neebo Introduces Native Snowflake Integration to Bridge Organization’s On-Premise Data Silos With Snowflake Data Cloud
We are excited to formally announce that Neebo provides native integration with Snowflake data warehouses!
Neebo’s native Snowflake integration performs advanced queries and searches directly in Snowflake. This not only saves analytics practitioners valuable time but also reduces network data costs compared to a generic integration. In addition, since Neebo provides virtualized access to data, it not only eliminates the need to move data (which keeps it secure) but also reduces the need for complex, expensive upfront data engineering projects. Neebo’s native integration and optimization with Snowflake eliminates the bottlenecks in distributed query providing faster response times to and from Snowflake virtual warehouses.
What is Native Snowflake Integration for Neebo Customers?
Neebo enables scalable and governed self-service analytics, utilizing native security and performance functionality, without the need to move data. Neebo’s native integration and optimization with Snowflake eliminates the bottlenecks in distributed query providing faster response times to and from Snowflake virtual warehouses.
Due to its inherent difficulty, the complexity of problems related to distributed query optimization continue to expand. To solve this issue, Neebo provides intelligent, native integration with Snowflake in two key areas – push down processing and smart caching.
Push Down Processing
With data storage continually growing, the cost of compute is also increasing. It becomes vital to look for alternate methods to improve query speed and efficiency. With Neebo’s pushdown query processing, SQL queries and transformation logic can be “pushed” to data residing in Snowflake in the form of generated SQL statements. So, rather than bringing the data to processing logic, Neebo takes the logic to where data resides in Snowflake.
Push down processing can be thought of as best-place processing for your data. The principle of pushdown (queries) is to process an operation that reduces the number of rows earlier in the process so that later processes – and computation resources – have to perform work on fewer rows. For example, Neebo will push SQL statements (i.e filter, join, etc.) deeper in the query order to inside a Snowflake warehouse to read all rows, remove rows that do not match the statement and then only perform the computation for the remaining rows. This saves the work from doing computation for the rows that do not match.
Smart Caching Option For Snowflake
Neebo instances can be configured to cache datasets using existing Snowflake data warehouses instead of using Neebo-native cloud storage. Neebo users not only have the option to run queries on uncached (direct connect) versions of their Snowflake tables but also to run queries on the cache.
Benefits of Neebo’s Smart Caching
Neebo’s cache infrastructure offers the following benefits over a direct connection to Snowflake:
- Compatibility with multiple database types
- Reduction in load time on Snowflake databases
- Leverage Materialized Views to further improve query performance
- Store large amounts of data with relatively low compute demand
- Lower variability in Snowflake compute demand
With data caching, Neebo provides unmatched performance tuning to Snowflake query performance. That allows users to fully optimize their Snowflake’s compute resources.
Using Neebo For Smart Cloud Analytics With Snowflake Data Cloud
Last week, Snowflake unveiled their Data Cloud – a one-stop shop where organizations can execute a full range of data-oriented tasks. Data Cloud is essentially an ecosystem of partners, customers, data providers, and data service providers meant to bridge data silos. In theory, Data Cloud should make it easier to migrate and provision data in Snowflake and remove the complexity of dealing with multiple cloud providers and regions. With Data Cloud, Snowflake hopes to become the hub of an organization’s data activities as they migrate their data to the cloud.
The benefits of public clouds are generally understood, and cloud migration from on-premise has accelerated in recent years. That said, on-premises data transfers can be complex and prone to failure. Cloud migration requires strategy and time. Most data in large organizations will be in a hybrid environment, with some data in public clouds and some data remaining on-prem.
With Neebo, the analytics community can discover, share, blend and perform analytics and ML, across cloud and on-premise data sets. Neebo is a virtual layer that bridges the two worlds. With Neebo, analysts can work on datasets spread across Snowflake Data Cloud and their organization’s legacy on-prem systems. Using Neebo, organizations take advantage of the instant, elastic, and unified data platform that Snowflake Data Cloud plans to offer while reducing the risks, time, and resources associated with migrating massive on-premise data workloads in the cloud.
Now you can harness Neebo to push down data wherever it resides directly into Snowflake for queries and processing. This new capability enables data and analytics teams to have easy access to large volumes of dispersed data. Data consumers directly query and configure Snowflake data via Neebo for their specific business needs. Then, they directly connect to or cache that data and analyze it in the business intelligence (BI) or machine learning (ML) tools of their choice.
See Native Snowflake Integration In Action
You can view a recording of our Snowflake webinar where we demonstrated our native Snowflake integration here. We also have live, instructor-led classes where you can try Neebo and Snowflake in a sandbox environment. To sign up for upcoming sessions convenient for you, head over to our events page.
Or test drive Neebo on your own to see how it can quickly answer ad-hoc analytics questions at neebo.ai/request-a-free-trial.