Presentation Unleashing Enterprise Potential: The Importance of Data Collection for Unveiling Opportunities and Driving Growth

© 2023 Cloudera, Inc. All rights reserved. 2 AGENDA 30min 1 2 3 Cloudera: The Hybrid Data Platform Company Enabling Analytics at Scale Q&A Streaming Data Lifecycle & Machine learning for Generative AI value creation

3 © 2022 Cloudera, Inc. All rights reserved. CLOUDERA - THE HYBRID DATA PLATFORM COMPANY Manage and secure the data lifecycle in any cloud or datacenter SECURITY | GOVERNANCE | CATALOG | METADATA | INTELLIGENCE 01 03 04 05 STREAMING & DATA FLOW DATA ENGINEERING DATA WAREHOUSE OPERATIONAL DATABASE MACHINE LEARNING & AI 02 Collect Enrich Report Serve Predict

© 2022 Cloudera, Inc. All rights reserved. 4 WE MANAGED 1 BILLION EVENTS PER SECOND

5 © 2022 Cloudera, Inc. All rights reserved. ENABLING ANALYTICS AND INSIGHTS ANYWHERE Driving enterprise business value REAL-TIME STREAMING ENGINE ANALYTICS & DATA WAREHOUSE DATA SCIENCE/ MACHINE LEARNING CENTRALIZED DATA PLATFORM STORAGE & PROCESSING ANALYTICS & INSIGHTS Stream Ingest Ingest – Data at Rest Deploy Models BI Solutions SQL Predictive Analytics • Model Building • Model Training • Model Scoring Actions & Alerts [SQL] Real-Time Apps STREAMING DATA SOURCES Clickstream Market data Machine logs Social ENTERPRISE DATA SOURCES CRM Customer history Research Compliance Data Risk Data Lending

© 2022 Cloudera, Inc. All rights reserved. 6 Enterprise Services Provisioning, Management and Monitoring Unified Security Edge-to-Enterprise Governance Single Sign-On Edge Management Edge data collection, Routing and monitoring MiNiFi Edge Flow Manager NiFi Registry Flow Management Enterprise data ingestion, transformation and enrichment Apache NiFi NiFi Registry Stream Processing Real-time stream processing at IoT scale Apache Kafka Schema Registry Streaming Analytics Predictive analytics and real-time insights Kafka Streams Apache Flink / SSB Spark Streaming Streams Messaging Manager Streams Replication Manager Streams Management

© 2022 Cloudera, Inc. All rights reserved. 7 Apache Kafka • Highly reliable distributed messaging system • Decouple applications, enables many-to-many patterns • Publish-Subscribe semantics • Horizontal scalable • Efficient implementation to operate at speed with big data volumes • Organized by topic to support several use cases Source System Source System Source System Kafka Hadoop Security Systems Real-Time Monitoring Source System Source System Source System Hadoop Security Systems Real-Time Monitoring Many-To-Many Publish-Subscribe Point-To-Point Request-Response

© 2022 Cloudera, Inc. All rights reserved. 8 KAFKA REPLICATION USE-CASES Disaster Recovery Geo-Locality Data Movement / Deployment Centralized Analytics Workload Isolation Legal / Compliance In an event of a partial or complete datacenter disaster, providing failover/failback to a secondary cluster in a different region / DC Active-active geo-localized deployments allows users to access a near-by data center to optimize their architecture for low latency and high performance. Use Kafka to synchronize data between on-prem applications and cloud deployments Aggregate data from multiple Kafka clusters into one location for organization-wide analytics Creation of different envs for SDLC: Dev, Test, Prod. Clusters for specific use case cases (ETL, ingestion, analytics, etc) Different data storage and security policies require clusters to be created in region but data still needs to be shared.

9 © 2022 Cloudera, Inc. All rights reserved. Example of Bounded and Unbounded join(s) Write Streaming Result to Kudu Join 2 Streaming User Event Topics Enrich Stream from Warehouse HR Table Enrich Stream from RT Mart Timesheet Table Filter & Transform

© 2019 Cloudera, Inc. All rights reserved. The Active Data Warehouse with Apache Kudu CDF IOT Devices Applications Metrics Logs & Files HDFS/ Object Storage Hot Storage Cold Storage SQL ○ s u p p o r t Real-Time Analytics Alerting Event Driven Applications Dashboards

© 2019 Cloudera, Inc. All rights reserved. LLMs at Cloudera

© 2022 Cloudera, Inc. All rights reserved. Data Cycle of a Generative AI Model DA Scope Adapt and Align model Application Integration Select Define the use case Choose an existing model Prompt Engineering Evaluate Integrate a model and build LLM- powered applications Optimise and deploy your model for inference Augmentatio n Fine Tuning Inspired by, Source Citation: Andrew Ng, DeepLearning.AI , Generative AI with LLMs course Data Collection and Preparation

13 © 2022 Cloudera, Inc. All rights reserved. HOW ARE LLMS DIFFERENT THAN ANYTHING BEFORE? Simplicity, speed and scale, over all your data! TECH SPARK / MAPREDUCE HIVE SQL / SQL LLMs PERSONAS/ SKILLS Programmers • Complex coding Analysts • Semantic queries Everyone • Natural language RESPONSE TIME Hours Seconds to minutes Milliseconds to seconds DATA PB Scale Most Data • Slower processing • Structured data • Semi-structured data TB scale with ETL High Value Data • Structured data • Rest is ETL-ed out PB scale All Data • Structured data • Semi-structured data • Unstructured data

© 2022 Cloudera, Inc. All rights reserved. 14 DEMO

© 2019 Cloudera, Inc. All rights reserved. 15 Dylienne Every - Engineer | SME ML & Cyber Security ✉ [email protected] Rein de Jong- Regional Vice President Benelux ✉ [email protected] 📞 +31 (0) 653 86 57 01 CONTACTS