Data Engineering Archives - Tiger Analytics

Blog Services: Data Engineering

Choosing the Right Agile Framework: A Data Engineer’s Guide for 12 Key Projects

Agile is fast becoming the rulebook for data engineers navigating high-stakes projects from migrations to real-time fraud detection. By tailoring frameworks like Scrum, Kanban, SAFe, or the Spotify Model to specific delivery challenges, teams can unlock speed, clarity, and resilience. In this blog, we share real-world project examples and best practices that show how the right Agile approach transforms data engineering outcomes.

The Data Leader’s Guide to Responsible AI: Why Strong Data Governance Is Key to Mitigating AI Risks

AI has moved from science fiction to everyday reality, but its success hinges on strong data governance. In this blog, we explore why effective governance is crucial for AI, how data leaders can build effective data governance for AI, and practical steps for aligning data governance with AI initiatives, ensuring transparency, mitigating risks, and driving better outcomes.

Navigating the Digital Seas: How Snowflake’s External Access Integration Streamlines Maritime Data Management

The maritime industry is increasingly adopting digital transformation to manage vast amounts of data from ships, sensors, weather, and third-party APIs. Snowflake’s External Access Integration simplifies this process by allowing seamless integration of real-time data without duplication. Read on to know how this feature works in practice and how it supports better, data-driven outcomes in the maritime sector.

Building Trusted Data: A Comprehensive Guide to Tiger Analytics’ Snowflake Native Data Quality Framework

Challenges in data quality are increasingly hindering organizations, with issues like poor integration, operational inefficiencies, and lost revenue opportunities. A 2024 report reveals that 67% of professionals don’t fully trust their data for decision-making. To tackle these problems, Tiger Analytics developed a Snowflake native Data Quality Framework, combining Snowpark, Great Expectations, and Streamlit. Explore how the framework ensures scalable, high-quality data for informed decision-making.

Exploring GitHub Copilot for Data Engineering

As GitHub Copilot’s popularity grows among Fortune 100 companies, we explore this AI-powered coding assistant’s potential impact on development workflows. Built by GitHub and OpenAI, this tool integrates with IDEs like Visual Studio Code, suggesting contextually relevant code snippets in real-time. While Copilot can boost productivity, developers must carefully review its suggestions to ensure they align with their needs. Read on to explore how this tool can enhance your coding experience.

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.

11 Ways Data Engineers Can Leverage an Agile Mindset to Help Drive Value Efficiently and Effectively

This comprehensive guide explores how Agile methodologies can be applied to data engineering within the Tiger Gene framework. It outlines key principles such as welcoming change, working in small increments, and continuous improvement. The article provides practical scenarios, pros and cons, and solutions for implementing Agile practices in data engineering projects. Data engineers can learn how to drive efficient and effective value through enhanced collaboration, flexibility, and iterative development, ultimately improving their project outcomes and team dynamics.

Implementing Context Graphs: A 5-Point Framework for Transformative Business Insights

This comprehensive guide outlines three phases: establishing a Knowledge Graph, developing a Connected Context Graph, and integrating AI for auto-answers. Learn how this framework enables businesses to connect data points, discover patterns, and optimize processes. The article also presents a detailed roadmap for graph implementation and discusses the integration of Large Language Models with Knowledge Graphs.

Connected Context: Introducing Product Knowledge Graphs for Smarter Business Decisions

Explore how Product Knowledge Graphs, powered by Neo4j, are reshaping data analytics and decision-making in complex business environments. This article introduces the concept of Connected Context and illustrates how businesses can harness graph technology to gain deeper insights, improve predictive analytics, and drive smarter strategies across various functions.

Solving Merchant Identity Extraction in Finance: Snowpark’s Data Engineering Solution

Learn how a fintech leader solved merchant identification challenges using Snowpark and local testing. This case study showcases Tiger Analytics’ approach to complex data transformations, automated testing, and efficient development in financial data processing. Discover how these solutions enhanced fraud detection and revenue potential.

A Comprehensive Guide to Pricing and Licensing on Microsoft Fabric

This comprehensive guide explores Microsoft Fabric’s pricing strategies, including capacity tiers, SKUs, and tenant hierarchy, helping organizations optimize their data management costs. It breaks down the differences between reserved and pay-as-you-go models, explaining Capacity Units (CUs) and providing detailed pricing information. By understanding these pricing intricacies, businesses can make informed decisions to fully leverage their data across various functions, leading to more efficient operations and better customer experiences.

Advanced Data Strategies in Power BI: A Guide to Integrating Custom Partitions with Incremental Refresh

Explore advanced data management strategies in Power BI through a detailed examination of integrating Custom Partitions with Incremental Refresh to efficiently handle large datasets. Key benefits such as improved query performance, more efficient data refresh, and better data organization are outlined, along with a practical guide on implementing these strategies in Power BI environments.

Blog Services: Data Engineering

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