Background of Implementation Playbook

BDB Professional Services group offers a comprehensive playbook for delivering end-to-end analytics projects, covering discovery, data modelling, ingestion, data science, visualization, testing, monitoring, and deployment. It supports modern architectures and integration with AI technologies to accelerate analytics implementations across industries.

• Purpose of the Playbook: It guides successful analytics project delivery on the BDB platform, including Discovery, Data Modelling, Ingestion, Data Science, Visualization, Testing, and Monitoring, with extensions for AI and LLM integration.

• Discovery Phase Essentials: Involves gathering business requirements, identifying stakeholders, defining functional and technical needs, and setting success criteria such as performance and scalability.

• Data Ingestion Patterns: Supports batch, streaming, API-based, and change data capture ingestion with best practices for each, using pipelines and jobs optimized for data volume and processing needs.

• Data Modelling and Architecture: Recommends choosing appropriate databases (MongoDB, ClickHouse, PostgreSQL, MySQL) based on data type and workload, and adopting Medallion Architecture (Bronze-Silver-Gold) or simpler 2-layer models according to complexity.

• Performance Optimization: Emphasizes indexing, partitioning, materialized views, data pruning, and sharding to enhance query speed, scalability, and manage large datasets efficiently.

• Data Science Integration: The Data Science Lab module supports project management, GPU acceleration, notebook integration, AutoML, feature store, and model deployment as APIs or pipeline components.

• Visualization Design: Offers report and dashboard modules with best practices on layout, interactivity, performance, naming conventions, version control, and migration strategies to ensure effective BI delivery.

• Data Agent Capabilities: The BDB Data Agent uses Agentic AI and LLMs for conversational analytics, anomaly detection, and actionable insights by training on selected datasets and knowledge bases.

• Testing and Monitoring: Covers unit, integration, performance, compatibility, and user acceptance testing, along with monitoring data ingestion, data quality, dashboard usage, and system performance using tools like Prometheus and Grafana.

• Deployment and Sizing: Supports cloud platforms (AWS, Azure, GCP, IBM) with detailed hardware and database sizing guidelines based on user concurrency and data volume, including examples for retail and telecom use cases with tailored architectures.