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For anyone in AI there is no big message that “data is the actual price”. If you have strong data foundations, your models and applications driven will be directly out of money.
But that’s where it’s messy. Building this foundation is not a piece of cake, especially if there are dozens of data sources, each organized by valuable information. For each source you have to build and mainain integration pipes – massive utilities for data teams Juggling ETL tools to centralize what is needed to power the workload AI. On the scale, the Tesy pipes become stiff narrow spines – hard to adapt, expand or expand.
Snowflake thinks he has an answer.
Today, at its annual summit, the company has announced the general availability of OpenFlow – a fully managed data service that cleans any type of data from virtually any source and flows the process of mobilizing information for fast AI.
How does it work?
OpenFlow, powered by Apache NIFI, uses connectors – preliminary or own – with built -in management and safety of Snowflake. Whether it is a non -moncodal multimodal Monte from the flows of the boxing or real -time events, the openflow connects, unifies and does all data types easily avid in the Cloud Data Snowflake AI.
“Data engineers often faced a critical compromise – if they wanted highly control pipes, promote complexity and significant infrastructure management. If they wanted simple solutions, they encourage limited privacy, flexibility and adaptation. OpenFlow meets lives to customers, provides flexibility and ensures the safety and management of public things” Engineering, narrated in division.
While Snowflake offers ingestion options such as Snowpipe for streaming or individual connectors, OpenFlow brings “understanding, effortlessly for ingestion of virtually all business data”.
“Snowppepe and streaming the Snowflake snow cabinet remains a key basis for customers who bring data to the snowflake, and focus on the“ load ”of the ETL process. OpenFlow, on the other hand, processes data extraction directly from the source system, also integrated with our new snow board architecture, but as soon as it is extraction, “he explained,” he explained, “he explained,” he explained, “he explained,” he explained, “he explained.
This will eventually unlock new cases of use, when AI can analyze a full picture of business data, including documents, images and real -time events, directly within the snowflake. Once the knowledge is extracted, they can return to the source system using the connector.
More than 200 available connectors
OpenFlow currently supports 200+ connectors and processors ready for learning, covers services such as boxing, Google Ads, Microsoft SharePoint, Oracle, Salesforce Data Cloud, Workday and Zendes.
“Integration Box with OpenFlow Snowflake OpenFlow… uses data extraction from the box using AI box, honors the original permissions for safe access and brings the data to the snowflake for analysis. It also serves a two -way flow in which enriched sounds or metadata can prove,” Ben, CTO, CTO, CTO. CTO, CTO, CTO, CTO, CTO, CTO, CTO, CTO, CTO, CTO, CTO, CTO, CTO, CTO, CTO, CTO, CTO, CTO, CTO, CTO, CTO, CTO, CTO, CTO, CTO, CTO, CTO, CTO, CTO, CTO, CTO.
Creating new connectors takes only a few minutes and speeds up the time to value. Users also acquire security functions such as role-based authorization, transit encryption and secrets to maintain end-to-end protection.
“An organization that requires real -time data integration deals with high volumes of data from different sources or relies on non -structural data, such as images, sound and video to derive from value, from which they will rise extremely from the openflow,” Child added. For example, a retail company could unite sales data, electronic trading, CRM and social media to provide personal experience and optimized operations.
Customers Snowflake Irwin, Securonix and Workwave are among those ready to use openflow to move and scalance of global data – although the company has been published by exact adoption numbers.
What will be next?
In the next step, Snowflake is to focus on opening the spine of intelligent data movement in real time across distributed systems that drive the age of agents.
“We focus on moving events on a massive scale and enabulization of two-way communication in real time, AGENT-AGENT-AGENS, so the knowledge and actions flow smoothly across distributed systems.
The timeline of these upgrades remains unclear for now.
