Track Custom Events with Google AnalyticsTech by Sunny Srinidhi - April 11, 2017April 11, 20170 You've probably heard of Google Analytics before. We all use the tool to track various things on our websites. The tool provides information such as the location of users, page views, the kind of devices and browsers used by those users, the age group, and a lot more. But what if you want to track certain events which are specific to your website? Say you want to track how many people filled a form, or how many people clicked a link on your website? Google Analytics provides an unbelievably simple way to track these custom events. It's actually just one line of code to track such events. Let's see how you'd do just this. When you create a Google Analytics account, the tool
Generative AI in Data Engineering: Transforming the LandscapeData Science by Sunny Srinidhi - January 3, 20250 Generative AI is revolutionizing data engineering by automating tasks, enhancing data quality, and enabling innovative solutions. This blog explores its fundamentals, practical applications like ETL automation and synthetic data generation, and its transformative impact on modern data workflows. It also offers insights on how data engineers can prepare, recommended learning resources, and comparisons with traditional methods. Embrace generative AI to stay ahead in the rapidly evolving data engineering landscape!
Understanding Data Mesh: A Paradigm Shift in Data ManagementData ScienceTech by Sunny Srinidhi - January 2, 2025January 2, 20250 Data Mesh is a decentralized approach to data management that treats data as a product and assigns ownership to domain-specific teams. By breaking away from centralized architectures like data warehouses and lakes, it ensures scalability, agility, and improved data quality. Emphasizing principles like domain-oriented ownership, self-serve infrastructure, and federated governance, Data Mesh enables faster insights, fosters collaboration, and breaks down silos. With applications across industries like e-commerce, healthcare, and finance, it transforms how organizations leverage data while addressing challenges like governance complexity and cultural shifts.
The Road Ahead: Key Data Engineering Trends for 2025Data ScienceTech by Sunny Srinidhi - December 31, 2024December 31, 20240 As we step into 2025, the world of data engineering is poised for transformative growth. From the rise of unified data architectures to the integration of AI-driven tools, the landscape is evolving faster than ever. This blog explores the key trends shaping the future—real-time data processing, edge computing, enhanced data governance, and more—while providing actionable insights on how professionals and organizations can adapt. Whether you’re a seasoned data engineer or just starting your journey, this comprehensive guide will help you navigate the challenges and seize the opportunities of 2025 with confidence.
Data Automation with AI/ML: A Comprehensive GuideData ScienceTech by Sunny Srinidhi - November 28, 20240 The article discusses the transformative impact of artificial intelligence (AI) and machine learning (ML) on data automation, enhancing efficiency, decision-making, and scalability in businesses. It explores trends like generative AI, AutoML, data governance, and democratization while providing real-world applications across various industries, ultimately guiding businesses in effective AI/ML integration.
Real-Time Data Processing: Understanding the What, Why, Where, Who, and HowData ScienceTech by Sunny Srinidhi - October 22, 20240 In today’s data-driven world, businesses and organizations are continuously generating massive amounts of data. While processing data in batch mode remains useful, the need for instant decision-making has led to an increasing focus on real-time data processing. This article delves into what real-time data processing is, why it's essential, its various applications, the tools used to achieve it, trends shaping its evolution, and real-world use cases. What is Real-Time Data Processing? Real-time data processing refers to the capability to continuously ingest, process, and output data as soon as it is generated, with minimal latency. Unlike batch processing, which collects and processes data in large groups at set intervals (e.g., daily or hourly), real-time processing works with data immediately as it becomes available,
Exploring the Inner Workings of Google BigQuery: A Deep Dive into Design, Competitors, Use Cases, and Pros/ConsData Science by Sunny Srinidhi - March 13, 2024March 13, 20240 Discover the inner workings of Google BigQuery, a game-changer in big data analytics. Unravel its architecture, including the prowess of its distributed query engine, Dremel, and the innovative Capacitor technology. Compare it with competitors, explore diverse use cases from real-time analytics to healthcare, and weigh its pros and cons. Join us on a journey into the heart of data analytics excellence.
Understanding the Battle of Database Storage: Row-Oriented vs. ColumnarData Science by Sunny Srinidhi - March 8, 2024March 8, 20241 In the realm of database storage, row-wise and columnar approaches stand as stalwarts with distinct advantages. Row-wise storage excels in transactional operations, ensuring data integrity with simplicity. Conversely, columnar storage revolutionizes analytical querying, leveraging vertical organization for rapid attribute retrieval. Understanding their nuances is pivotal in crafting efficient, tailored database solutions for diverse data-driven needs.
Cleaning and Normalizing Data Using AWS Glue DataBrewData Science by Sunny Srinidhi - January 17, 2022January 17, 20221 In this post, we’ll see what is AWS Glue DataBrew and how to use it to clean and transform our data in a data pipeline.
Getting Started With JanusGraphData Science by Sunny Srinidhi - February 25, 2021February 25, 20211 JanusGraph is a graph processing tool that can query distributed graph data in milliseconds. In this post, we’ll see how to get started with it.