Technical infrastructure and implementation patterns
An overview of the various privacy modes and consent management features in Piwik Pro, a popular web analytics platform. Covers techniques for enabling session-only tracking and ensuring compliance with GDPR and CCPA regulations.
Techniques and best practices for implementing client-side data tracking on web and mobile applications, including SDKs, virtual URLs, element selectors, event throttling, and handling browser quirks like ad-blockers and ITP/ETP.
This article provides an introduction to server-side tracking, highlighting the potential benefits and drawbacks of this approach compared to traditional client-side tracking. It covers the technical aspects of server-side data collection, including latency, schema, and cost trade-offs, as well as the implications for data privacy and compliance.
Exploring the key features and trade-offs of column-store data warehouse engines like Snowflake, BigQuery, and Redshift. Understand partitioning, clustering, compute-storage separation, and query optimization strategies for performance and cost optimization.
An in-depth look at the challenges of creating a scalable, multi-site Piwik Pro setup, with a focus on tag management, data layer design, and instrumentation governance. This article provides technical insights and best practices for teams looking to implement enterprise-grade tracking solutions across complex web properties.
Learn how to detect anomalies in machine learning using Python. Explore key techniques with code examples and visualizations in PyCharm for data science tasks.
This article explores the practical application of the 4 transformation layers (raw, staging, dimensional, and reporting) in a data warehouse architecture. It covers topics like fact/dimension modeling, materialized views, and pipeline orchestration to ensure structure and maintainability in your data infrastructure.
A comprehensive guide on designing and implementing an in-house feature flagging and A/B testing platform for managing product rollouts, experiments, and user segmentation at scale.
Explores the concept of semantic layers in business intelligence (BI) tools, which provide a governed and reusable data model for dashboards and reports. Covers topics like LookML, Power BI datasets, dbt Metrics, and how to build universal definitions, join logic, and shared dimensions/measures that can be leveraged across multiple BI platforms.
This article provides insights on how to reduce BigQuery costs by clustering the GA4 events table. It covers key data warehousing concepts like partitioning, clustering, and query optimization to help data professionals manage their BigQuery usage and costs more effectively.
Best practices for implementing secure and governed data infrastructure, including role-based access controls, encryption, audit logging, and GDPR/CCPA compliance policies.
This article explores how Apple's Intelligent Tracking Protection (ITP) and other privacy regulations like GDPR and CCPA impact data collection and tracking for marketing analytics. It covers best practices for ensuring data compliance, obtaining user consent, and adapting tracking methods to a more privacy-focused environment.
Explore how customer data platforms (CDPs) can be leveraged to build real-time, privacy-compliant data pipelines that stitch together user identities and events across digital touchpoints.
This article discusses the technical approaches and considerations for resolving user identities across devices and touchpoints, including anonymous-to-user joins, deterministic vs. probabilistic linking, and managing identity hierarchies and conflict resolution logic. It covers the role of customer data platforms (CDPs) in stitching customer profiles and how to ensure data quality, privacy, and compliance in these identity-matching processes.
Modular Dimensional Data Modeling offers a revolutionary approach to data engineering, empowering analytics professionals to build scalable, maintainable data models. By leveraging modular design principles and dimensional modeling techniques, this methodology enables streamlined data transformation, effortless integration of new data sources, and enhanced data accessibility for business intelligence and advanced analytics. For data-driven organizations seeking to unlock the full potential of their data, Modular Dimensional Data Modeling provides a strategic edge in navigating the complexities of modern data ecosystems.
Struggling with dashboard overload? This blog post offers a solution to the problem of "dashboard sprawl" - the proliferation of dashboards that can overwhelm data teams. By introducing techniques for consolidating and streamlining your dashboard ecosystem, you can enhance data visibility, improve decision-making, and free up valuable time for deeper analytics work. Data professionals seeking to optimize their reporting infrastructure and enhance the impact of their analytics will find this insight particularly compelling.
Knowledge graphs are revolutionizing how we analyze and understand language. By modeling the complex relationships between entities, concepts, and text, these powerful tools enable data scientists, machine learning engineers, and NLP researchers to uncover deeper insights and develop more robust language models. Leverage the power of knowledge graphs to unlock new possibilities in text mining, question answering, and semantic reasoning, transforming the way you approach natural language processing challenges.
While large language models have impressive capabilities, they are not a panacea for all data challenges. This blog post underscores the importance of understanding the limitations of these models and leveraging specialized techniques and tools to tackle complex data problems effectively. Data professionals, including data engineers, analysts, and business intelligence analysts, can benefit from this insight to optimize their data management and analysis workflows.