Data Warehousing Trends & Emerging Technologies PDF

Data Warehousing Emerging Technology in Datawarehousing  Trends in Datawarehousing Shift to cloud-based data warehouses Example: Netflix migrated from on-premises systems to Amazon Web Services (AWS) for its data warehousing. This shift allowed them to handle...

Data Warehousing Emerging Technology in Datawarehousing  Trends in Datawarehousing Shift to cloud-based data warehouses Example: Netflix migrated from on-premises systems to Amazon Web Services (AWS) for its data warehousing. This shift allowed them to handle massive, global data processing needs with scalable, cost-efficient cloud resources. It improved real-time analytics, enhanced personalized recommendations, and supported dynamic scaling during peak traffic, like major show releases. Focus on real-time data processing Example: Amazon uses real-time data processing to manage inventory and optimize pricing. Through tools like Apache Flink, they analyze data from millions of transactions in real time to adjust product availability, predict demand, and offer personalized discounts instantly. Apache Flink is an open-source, distributed stream processing framework designed for real-time data processing. It allows companies to process large volumes of data in real time with low latency. Flink is often used for applications like event-driven architectures, real-time analytics, and complex event processing.  Trends in Datawarehousing Growing importance of AI and machine learning integration Example: Spotify uses AI and machine learning to personalize music recommendations for users. By analyzing listening patterns and user behavior, their algorithms provide tailored playlists like "Discover Weekly," enhancing user engagement and satisfaction. AI also helps optimize playlists in real time based on listening trends. Rise of decentralized data architectures (e.g., data mesh) Example: BMW shifted to a data mesh to break down silos across its global operations. With different departments managing their own data (e.g., manufacturing, customer service, and sales), each team owns the data for its specific domain. This decentralized approach improved data accessibility, speed, and innovation, enabling faster decision-making and more personalized customer experiences. Data Mesh is a decentralized data architecture designed to address the challenges of scaling data systems in large, complex organizations.  Cloud Datawarehousing Technologies: Snowflake, Google BigQuery, Amazon Redshift Benefits: scalability, cost-efficiency, ease of maintenance Example: adoption rates and success stories  AI/ Machine Learning Integration How ML is transforming data warehousing: Intelligent query optimization Predictive analytics integration Example technologies: AWS SageMaker, Databricks AI Example: Walmart integrated machine learning (ML) into its data warehouse. This allowed the company to run predictive models directly on their data.  Automation in Datawarehousing Automated schema design and ETL/ELT processes Technologies: dbt(Data Build Tool), Matillion, Airflow, Python (pandas) Example: reducing human intervention in maintenance  Automation in Datawarehousing How ML is transforming data warehousing: Intelligent query optimization Predictive analytics integration Example technologies: AWS SageMaker, Databricks AI Example: Walmart integrated machine learning (ML) into its data warehouse. This allowed the company to run predictive models directly on their data.