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Snowflake & TimeXtender

The Fastest Way to Build Data Solutions on Snowflake

Snowflake offers scalability, flexibility, and performance for cloud data warehousing but presents challenges like manual coding, managing compute costs, and orchestrating workflows. TimeXtender uses metadata, AI, and a low-code interface to streamline data ingestion, transformation, and delivery, integrating seamlessly with Snowflake for faster, cost-effective, and governed solutions.

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Snowflake

Implement Snowflake 10x Faster with TimeXtender

Snowflake delivers powerful cloud-based data warehousing, but its complexity often results in slow, costly, and inefficient implementations. Data loading and transformation typically rely on extensive manual coding in SQL and Python, while managing virtual warehouse costs and orchestrating workflows often requires third-party tools, adding to the complexity. TimeXtender’s Holistic Data Suite automates and simplifies these processes, making it essential for organizations looking to fully leverage Snowflake’s scalability and performance.

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TimeXtender Data Integration

Simplifying Complexity and Automating Workflows, Unified Data Integration for Cloud Workflows, Pre-Built Connectors for Cloud and On-Premises Sources, Low-Code ELT Automation, Incremental Data Loading, Automated Dimensional Modeling, Metadata-Driven Governance and Lineage

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Snowflake provides native tools like Snowpipe, COPY Command, and Streams and Tasks for data ingestion, but these tools can be limited in scope and require extensive SQL scripting and configuration. TimeXtender Data Integration simplifies these processes by providing a unified, automated approach that streamlines data ingestion, transformation, and loading without the need for extensive coding or multiple tools.

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Unified Data Integration for Cloud Workflows:

Seamlessly ingest data from SaaS applications, APIs, on-premises databases, cloud platforms, and custom sources into Snowflake using a centralized, metadata-driven approach. This eliminates the need for multiple ingestion tools and ensures consistent workflows​.

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Pre-Built Connectors for Cloud and On-Premises Sources:

Leverage an extensive library of pre-built connectors to integrate data from a wide range of sources into Snowflake, eliminating custom coding and accelerating ingestion.

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Low-Code ELT Automation:

Design complex ELT workflows with a drag-and-drop interface. TimeXtender automatically generates optimized SQL code for Snowflake, reducing the need for manual SQL and Python scripting and accelerating development​.

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Incremental Data Loading:

Load only new or updated data into Snowflake, reducing processing times, optimizing virtual warehouse usage, and controlling compute costs​.

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Automated Dimensional Modeling:

Automatically build and maintain star schemas and other dimensional models in Snowflake, optimizing data for efficient analysis and reporting with BI tools like Qlik, Tableau, and Power BI​.

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Metadata-Driven Governance and Lineage:

Capture, document, and synchronize metadata across your Snowflake environment to automate data lineage tracking and documentation, ensure compliance, and maintain governance policies​.

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TimeXtender Master Data Management

Centralizing and Governing Critical Data, Centralized Master Data Control, Automated Synchronization with Snowflake, Hierarchy and Relationship Management

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Snowflake is excellent for storing and querying data, but it does not provide built-in capabilities for defining and maintaining master data entities, making it difficult to maintain consistency across datasets. TimeXtender Master Data Management solves this by centralizing and automating master data governance.

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Centralized Master Data Control:

Manage business entities such as customers, products, and suppliers through a unified interface, eliminating the need for spreadsheets and custom scripts​.

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Automated Synchronization with Snowflake:

Keep master data up-to-date and synchronized across Snowflake tables and other systems, ensuring data consistency and accuracy​.

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Hierarchy and Relationship Management:

Define and manage complex master data hierarchies and relationships to support accurate analytics and reporting within Snowflake​.

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TimeXtender Data Quality

Ensuring High-Quality Inputs for Accurate Insights, Rule-Based Data Validation, Anomaly Detection and Alerts, Automated Data Quality Monitoring, Compliance and Audit Trails

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Snowflake offers Data Metric Functions (DMFs) for monitoring data quality metrics such as data freshness, duplicates, NULL values, and unique counts. However, these functions are limited to assessing data within Snowflake and require manual configuration. Managing data quality across multiple systems and ensuring comprehensive validation still often requires third-party solutions. TimeXtender Data Quality automates data validation, anomaly detection, and monitoring across Snowflake and other systems, ensuring accurate and reliable data throughout your entire data pipeline.

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Rule-Based Data Validation:

Define and enforce tailored data quality rules using a low-code interface to ensure data accuracy before it enters Snowflake, preventing flawed data from affecting analytics​.

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Anomaly Detection and Alerts:

Automatically identify data anomalies and inconsistencies in real time, with alerts for prompt investigation and resolution​.

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Automated Data Quality Monitoring:

Continuously monitor data quality across Snowflake and other systems to ensure clean, trustworthy data for reporting, analysis, and decision-making.

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Compliance and Audit Trails:

Maintain detailed audit trails of data validation activities to support compliance with regulations like GDPR and HIPAA​.

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TimeXtender Orchestration

Automating Processes and Optimizing Resources, Visual Workflow Designer, Task Scheduling and Monitoring, End-to-End Workflow Orchestration, Cost Optimization for Virtual Warehouses

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Managing workflows in Snowflake can be handled with native tools like Streams, Tasks, and Snowpipe, but orchestrating complex, end-to-end workflows often requires additional effort and configuration. TimeXtender Orchestration simplifies this by providing a centralized, low-code interface for automating data ingestion, transformation, and delivery. With intuitive visual design, task scheduling, and dynamic resource optimization, TimeXtender reduces manual effort and helps control virtual warehouse costs, ensuring efficient and consistent execution.

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Visual Workflow Designer:

Design and manage workflows using an intuitive drag-and-drop interface, eliminating the need for complex scripting​.

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End-to-End Workflow Orchestration:

Automate data ingestion, transformation, and delivery workflows across Snowflake and other systems, ensuring consistent execution and reducing manual effort​.

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Task Scheduling and Monitoring:

Schedule and monitor data workflows to ensure timely execution, with visibility into task status and performance metrics​.

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Cost Optimization for Virtual Warehouses:

Automatically scale Snowflake virtual warehouses up or down based on workload demands and deactivate idle resources to minimize compute costs​.

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