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Your Journey Towards Real-Time ML Starts Now
Available to all Snowflake customers, Vista acts as the central hub for ML features, allowing data teams to define features as code using Python and SQL and then automating production-grade ML data pipelines to generate accurate training datasets and serve up-to-date features online for real-time inference
Made Easy with Vista
How It works
Key Challenges of Production ML
- Training-serving skew
- Point-in-time correctness
- Productionizing notebooks
- Real-time transformations
- Melding batch + real-time data
- Latency constraints
- Data scientist and data engineering siloed workflows
- Limited discovery and re-use of features across teams
Build more powerful models by easily incorporating batch, streaming, and real-time data.
Deliver value from real-time models in minutes rather than months.
Continuously improve and iterate on production MLmodels across teams and use cases.
Vista’s platform puts our robust integration capabilities directly in your hands, and increases the velocity of data teams.
An automated hub for your external data
- Connect to your licensed or public external data sources via FTP, SFTP, GCS, or S3
- Decide when data is delivered according to your schedule
- Deliver to BigQuery, Snowflake, GCS, S3, FTP, and SFTP
A faster way to data operations maturity
- Automatically profile data to detect delivery patterns
- Manage changes and map multiple schemas
- Backfill data from any date range available from your data supplier
- Receive data delimited in the format you prefer
A source of truth and data reliability
- Monitor the status of all active data pipelines in the APP data health dashboard
- Receive alerts and triage info for data quality and availability issues
- Role-based access controls available to fit your teams needs
The Vista Platform was built for data teams with an insatiable appetite for external data
The complete SaaS ELT platform for Snowflake
Kivyo addresses the entire journey of loading, transforming, and activating your data with Snowflake, by providing a unified, SaaS solution for both technical and non-technical users. With Snowflake & Kivyo, you can focus on your business, not on managing your data & pipelines.
A complete data solution
Kivyo provides all of your integration needs in a single solution: Extraction, Transformation (ELT), Change Data Capture, Data Activation / Reverse ETL, and Data Operations. Everything works together in harmony, giving you fast integrations and easy insights.
That's easy to use & maintain
Get started in minutes with Snowflake Partner Connect, and easily build advanced & reliable data pipelines without code. Snowflake integrations-made- easy with 200+ fully-managed connectors & no-code custom connectors. Instantly install complete Snowflake data solutions with our Starter Kits.
And powerful enough to handle complex data needs
Snowpark & native Python support for infinite extendibility. Advanced workflow orchestration & scheduling. Developer friendly with multiple environments, Cl/CD support, and version control. Integration friendly & easily extensible with our robust API and CLI tools.
Why Kivyo for Snowflake?
200+ fully managed connectors
Get data from any source, avoid updating APIs, and connect to every system.
Support for CDC & SQL based replication
Migrate your database to the cloud, efficiently and without disruption.
Accelerate onboarding, create pipelines in minutes, and become data driven.
Custom file zones
Take control of your data storage and enhance security and privacy.
Snowflake-ready technology validation partner
How Vista Helped Kivyo Save 25% Per Month in Snowflake Costs
Managing Snowflake Costs at DTCC
Step 1 - Setup and Integration
The customer first integrated Vista’s platform with their Snowflake data warehouse and Looker dashboards and charts. This allowed Acryl to analyze DTCC Media’s data usage, lineage and other signals to estimate Snowflake costs.
Step 2 - Implementation of Metadata Tests
Step 3 - Elimination of Redundant Data
Step 4 - Continuous Monitoring and Optimization
Metadata Tests are built for continuous monitoring of rules on top of Vista’s metadata graph. By implementing these tests, DTCC ensures that data cleanup efforts are not limited to a one-time occurrence. Instead, future inefficiencies will be proactively identified and addressed
to prevent cost overruns.