With advancements in artificial intelligence (AI) and machine learning (ML), the demand for real-time and accurate data ingestion has reached new heights. AI and ML models, require a constant feed of high-quality data to train, adapt, and deliver accurate insights and predictions. Consequently, organizations must prioritize robust data ingestion strategies to harness the full potential of their data assets and stay competitive in the AI-driven era.

Challenges with Existing Data Ingestion Mechanisms

While platforms like Snowflake offer powerful data warehousing capabilities, the native data ingestion methods provided by Snowflake, such as Snowpipe and the COPY command, often face limitations that hinder scalability, flexibility, and efficiency.

Limitations of the COPY Method

• Data Transformation Overhead: Extensive transformation during the COPY process can introduce overhead, which is better performed post-loading.
• Limited Horizontal Scalability: COPY struggles to scale efficiently with large data volumes, underutilizing warehouse resources.
• File Format Compatibility: Complex formats like Excel require preprocessing for compatibility with Snowflake’s COPY INTO operation.
• Data Validation and Error Handling: Snowflake’s validation during COPY is limited; additional checks can burden performance.
• Manual Optimization: Achieving optimal performance with COPY demands meticulous file size and concurrency management, adding complexity.

Limitations of Snowpipe

• Lack of Upsert Support: Snowpipe lacks direct upsert functionality, necessitating complex workarounds.
• Limited Real-Time Capabilities: While near-real-time, Snowpipe may not meet the needs for instant data availability or complex streaming transformations.
• Scheduling Flexibility: Continuous operation limits precise control over data loading times.
• Data Quality and Consistency: Snowpipe offers limited support for data validation and transformation, requiring additional checks.
• Limited Flexibility: Snowpipe is optimized for streaming data into Snowflake, limiting custom processing and external integrations.
• Support for Specific Data Formats: Snowpipe supports delimited text, JSON, Avro, Parquet, ORC, and XML (using Snowflake XML format), necessitating conversion for unsupported formats.

Tiger’s Snowpark-Based Framework – Transforming Data Ingestion

To address these challenges and unlock the full potential of data ingestion, organizations are turning to innovative solutions that leverage advanced technologies and frameworks. One such solution we’ve built, is Tiger’s Snowpark-based framework for Snowflake.

Our solution transforms data ingestion by offering a highly customizable framework driven by metadata tables. Users can efficiently tailor ingestion processes to various data sources and business rules. Advanced auditing and reconciliation ensure thorough tracking and resolution of data integrity issues. Additionally, built-in data quality checks and observability features enable real-time monitoring and proactive alerting. Overall, the Tiger framework provides a robust, adaptable, and efficient solution for managing data ingestion challenges within the Snowflake ecosystem.

Key features of Tiger’s Snowpark-based framework include:

Configurability and Metadata-Driven Approach:

• Flexible Configuration: Users can tailor the framework to their needs, accommodating diverse data sources, formats, and business rules.
• Metadata-Driven Processes: The framework utilizes metadata tables and configuration files to drive every aspect of the ingestion process, promoting consistency and ease of management.

Advanced Auditing and Reconciliation:

• Detailed Logging: The framework provides comprehensive auditing and logging capabilities, ensuring traceability, compliance, and data lineage visibility.
• Automated Reconciliation: Built-in reconciliation mechanisms identify and resolve discrepancies, minimizing errors and ensuring data integrity.

Enhanced Data Quality and Observability:

• Real-Time Monitoring: The framework offers real-time data quality checks and observability features, enabling users to detect anomalies and deviations promptly.
• Custom Alerts and Notifications: Users can set up custom thresholds and receive alerts for data quality issues, facilitating proactive monitoring and intervention.

Seamless Transformation and Schema Evolution:

• Sophisticated Transformations: Leveraging Snowpark’s capabilities, users can perform complex data transformations and manage schema evolution seamlessly.
• Adaptability to Changes: The framework automatically adapts to schema changes, ensuring compatibility with downstream systems and minimizing disruption.

Data continues to be the seminal building block that determines the accuracy of the output. As businesses race through this data-driven era, investing in robust and future-proof data ingestion frameworks will be key to translating data into real-world insights.

The post Tiger’s Snowpark-Based Framework for Snowflake: Illuminating the Path to Efficient Data Ingestion appeared first on Tiger Analytics.

Their workday is consumed by the arduous task of migrating complex Excel data from legacy systems to Snowflake. They spend hours grappling with detailed Excel files, trying to navigate through multiple headers, footers, subtotals, formulas, macros, and custom formatting. The manual process is time-consuming, and error-prone, and hinders their ability to focus on deriving valuable insights from the data.

To streamline their workday, the data analyst can leverage Snowpark Python’s capabilities to streamline the process. They can effortlessly access and process Excel files directly within Snowflake, eliminating the need for external ETL tools or complex migration scripts. With just a few lines of code, they can automate the extraction of data from Excel files, regardless of their complexity. Formulas, conditional formatting, and macros are handled seamlessly, ensuring data accuracy and consistency.

Many businesses today grapple with the complexities of Excel data migration. Traditional ETL scripts may suffice for straightforward data migration, but heavily customized processes pose significant challenges. That’s where Snowpark Python comes into the picture.

Snowpark Python: Simplifying Excel Data Migration

Snowpark Python presents itself as a versatile tool that simplifies the process of migrating Excel data to Snowflake. By leveraging Snowpark’s file access capabilities, users can directly access and process Excel files within Snowflake, eliminating the need for external ETL tools or complex migration scripts. This approach not only streamlines the migration process but also ensures data accuracy and consistency.

With Snowpark Python, businesses can efficiently extract data from Excel files, regardless of their complexity. Python’s rich ecosystem of libraries enables users to handle formulas, conditional formatting, and macros in Excel files. By integrating Python scripts seamlessly into Snowflake pipelines, the migration process can be automated, maintaining data quality throughout. This approach not only simplifies the migration process but also enhances scalability and performance.

Tiger Analytics’ Approach to Excel Data Migration using Snowpark Python

At Tiger Analytics, we‘ve worked with several Fortune 500 clients on data migration projects. In doing so, we’ve found a robust solution: using Snowpark Python to tackle this problem head-on. Here’s how it works.

We crafted Snowpark code that seamlessly integrates Excel libraries to facilitate data loading into Snowflake. Our approach involves configuring a metadata table within Snowflake to store essential details such as Excel file names, sheet names, and cell information. By utilizing Snowpark Python and standard stored procedures, we have implemented a streamlined process that extracts configurations from the metadata table and dynamically loads Excel files into Snowflake based on these parameters. This approach ensures data integrity and accuracy throughout the migration process, empowering businesses to unlock the full potential of their data analytics workflows within Snowflake. So we’re able to not only accelerate the migration process but also future-proof data operations, enabling organizations to focus on deriving valuable insights from their data.

The advantage of using Snowpark Python is that it enables new use cases for Snowflake customers, allowing them to ingest data from specialized file formats without the need to build and maintain external file ingestion processes. This results in faster development lifecycles, reduced time spent managing various cloud provider services, lower costs, and more time spent adding business value.

For organizations looking to modernize data operations and migrate Excel data from legacy systems into Snowflake, Snowpark Python offers a useful solution. With the right partners and supporting tech, a seamless data migration will pave the way for enhanced data-driven decision-making.

The post Migrating from Legacy Systems to Snowflake: Simplifying Excel Data Migration with Snowpark Python appeared first on Tiger Analytics.

For instance, business owners need to answer over 40 different questions when they apply for worker’s compensation insurance. In addition, it could take many weeks of constant emailing between insurance companies and businesses after submission! Such bottlenecks lead to poor customer experiences while significantly impacting the quote-to-bind ratio for insurers. Furthermore, over 20% of the information captured from businesses and agents is inaccurate – resulting in premium leakage and poor claims experience.

The emergence of data prefill – and the challenges ahead

Today, more insurers are eager to pre-populate their commercial underwriting applications by using public and proprietary data sources. The data captured from external sources help them precisely assess risks across insurance coverages, including Workers Compensation, General Liability, Business Property, and Commercial Auto. For example, insurers can explore company websites and external data sources like Google Maps, OpenCorporates, Yelp, Zomato, Trip Advisor, Instagram, Foursquare, Kompass, etc. These sources provide accurate details, such as year of establishment, industry class, hours of operation, workforce, physical equipment, construction quality, safety standards, and more.

However, despite the availability of several products that claim to have successfully prefilled underwriting data, insurance providers continue to grapple with challenges like evolving business needs and risks, constant changes in public data format, ground truth validation, and legal intricacies. Sources keep evolving over time both in terms of structure and data availability. Some even come with specific legal constraints. For instance, scraping is prohibited by many external websites. Moreover, the data prefill platform needs to fetch data from multiple sources, which requires proper source prioritization and validation.

Insurers have thus started to consider building custom white-box solutions that are configurable, scalable, efficient, and compliant.

Creating accurate, effortless, and fast commercial underwriting journeys

The futuristic data prefill platforms can empower business insurance providers to prefill underwriting information effortlessly and accurately. These custom-made platforms are powered by state-of-art data matching and extraction frameworks, a suite of advanced data science techniques, triangulation algorithms, and scalable architecture blueprints. The platform empowers underwriters to directly extract data from external sources with a high fill rate and great speed. Where the data is not directly available, the ML classifiers help predict underwriting questions for underwriters with high accuracy.

Tiger Analytics has assisted in custom-building such AI-led underwriting data prefill solutions to support various commercial underwriting decisions for leading US-based Worker’s compensation insurance providers. Our data prefill solution uses various AWS services such as AWS Lambda, S3, EC2, Elastic Search, Sage maker, Glue, Cloudwatch, RDS, and API Gateway; which ensures increased speed-to-market and scalability – with improvements gained through incremental addition of each source. It is a highly customizable white-box solution with a built-in Tiger’s philosophy of Open IP. Using AWS services allows the solution to be quickly and cost-effectively tweaked to cater to any changes in external source formats. Delivered as an AWS cloud-hosted solution, this solution uses AWS Lambda architecture to enable scale and state-of-the-art application orchestration engine to prefill data for commercial underwriting purposes.

Key benefits

• Unparalleled accuracy of 95% on all the data provided by the platform
• Over 90% fill rate
• Significant cost savings of up to $10 million annually
• Accelerated value creation by enabling insurers to start realizing value within 3-6 months

Insurers must focus on leveraging external data sources and state-of-the-art AI frameworks, data science models, and data engineering components to prefill applications. And with the right data prefill platform, insurers can improve the overall quote-to-bind ratio, assess risks accurately and stay ahead of the competition. 

The post Revolutionizing SMB Insurance with AI-led Underwriting Data Prefill Solutions appeared first on Tiger Analytics.