Blog Tags: Data Engineering
What is Data Observability Used For?
Learn how Data Observability can enhance your business by detecting crucial data anomalies early. Explore its applications in improving data quality and model reliability, and discover Tiger Analytics’ solution. Understand why this technology is attracting major investments and how it can enhance your operational efficiency and reduce costs.
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In Digital, We Trust: A Deep Dive into Modern Data Privacy Practices
Explore the interplay between data utilization and privacy in fostering digital trust. Uncover key measures like Data Classification and Encryption, and compare encryption practices on AWS and GCP. Real-world scenarios illustrate applied privacy considerations in tech-driven exchanges.
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A Comprehensive Guide: Optimizing Azure Databricks Operations with Unity Catalog
Learn how Unity Catalog in Azure Databricks simplifies data management, enabling centralized metadata control, streamlined access management, and enhanced data governance for optimized operations.
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Enabling Cross Platform Data Observability in Lakehouse Environment
Dive into data observability and its pivotal role in enterprise data ecosystems. Explore its implementation in a Lakehouse environment using Azure Databricks and Purview, and discover how this integration fosters seamless data management, enriched data lineage, and quality monitoring, empowering informed decision-making and optimized data utilization.
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Unleash the Full Potential of Data Processing: A Roadmap to Leveraging Databricks
Efficient data processing is vital for organizations in today’s data-driven landscape. Data ingestion service, Databricks Auto Loader, streamlines the complex data loading process, saving time and resources. Learn how Tiger Analytics used Databricks to manage massive file influx and enable near real-time processing, enhancing data quality and accelerating decision-making.
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A Practical Guide to Setting Up Your Data Lakehouse across AWS, Azure, GCP and Snowflake
Explore the evolution from Enterprise Data Warehouses to Data Lakehouses on AWS, Azure, GCP, and Snowflake. This comparative analysis outlines key implementation stages, aiding organizations in leveraging modern, cloud-based Lakehouse setups for enhanced BI and ML operations.
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How to Design your own Data Lake Framework in AWS
Learn how you can efficiently build a Data Lakehouse with Tiger Data Fabric’s reusable framework. We leverage AWS’s native services and open-source tools in a modular, multi-layered architecture. Explore our insights and core principles to tailor a solution for your unique data challenges.
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Unlocking the Potential of Modern Data Lakes: Trends in Data Democratization, Self-Service, and Platform Observability
Learn how self-service management, intelligent data catalogs, and robust observability are transforming data democratization. Walk through the crucial steps and cutting-edge solutions driving modern data platforms towards greater adoption and democratization.
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Revolutionizing Business Intelligence: Trends, Tools, and Success Stories Unveiled by Tiger’s BI Framework
Uncover Modern BI’s impact with real-world cases. Learn how embedded BI resolves scattered stacks, harnessing Big Data for insights. Explore Tiger’s BI Framework, Dashboard Program, and Metadata Extractor, enabling data democratization for transformative solutions.
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Managing Parallel Threads: Techniques with Apache NiFi
Control parallel thread execution via Apache NiFi for efficient data flow management using new methods to optimize performance, handle concurrent tasks, and ensure system stability. Be equipped to enhance your data processing workflows with advanced threading strategies.
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