........ 371 9.7. Commercial Support .......................... 372 9.7.1. Professional Support Services from Canonical ................................ 372 9.7.2. The Canonical Marketplace ..... 373 documentation Online Documentation Community Support Launchpad The Fridge Paid for Commercial Services Lesson Summary Review Exercise Partitioning and Booting - optional 60 What is Partitioning Encourages customisation and variation as opposed to a one size fits many approach. • Relies on a 'services attached' business model rather than per license and seat basis. • Believes that the benefits of

Jianfei Hu, Google Cloud Next ‘19 #IstioCon Why Add VMs to the Mesh? ● = Why Service Mesh? ○ More services = more complexity ○ Need consistent policy enforcement ○ Need consistent metrics aggregation ● Traffic management ○ Load balancing for VMs, failover, A/B testing, modern rollouts for VM services ● Security ○ Enforce the same policies in the same way, across compute environments ● Observability Emerging Use Cases #IstioCon Legacy Scenarios ● Stateful applications ○ Data store ● Legacy software ○ Financial services ○ Enterprise/Workshop applications ○ Hard to lift and shift ● Packaged

● More than 5,000 Microservices ranging from ○ API services, Search Engine, etc. ○ Databases, Key-Value stores - Oracle, MySQL, etc. ○ Big data systems & Pipelines - Hadoop, Apache Spark, Apache Flink GPUs #IstioCon Application Deployment: Cloud Layout ● Region: A metro region ● DC: One or more Data Centers in each Region ● AZ: One or more Availability Zones in each DC ○ Independent power, cooling peering with the Internet closer to the customer ○ PoPs are mini AZs Region R1 AZ 1 AZ 2 AZ n Data Center DC1 Region Rn #IstioCon Application Deployment: Cloud Layout ● Multiple K8s Clusters

Project security Editors overview Data Editor overview Data View and Format Data Filters Data viewing and editing Data refresh Navigation Managing Panels Managing Data formats Managing Charts Projects in DBeaver Data Editor tools Viewing and editing data Data operations DBeaver Ultimate User Guide 24.2.ea. Page 8 of 1171. Working with Spatial Geographic Information System (GIS) data GIS examples examples Working with XML and JSON Virtual column expressions Mock Data generation SQL Editor overview SQL Execution Query execution plan Script management SQL Assist and Auto Complete SQL templates

Editors overview Data Editor overview Data View and Format Data Filters Data viewing and editing Data refresh Navigation Managing Panels Projects workspace Editors in DBeaver Data Editor tools Viewing Viewing and editing data Data operations DBeaver User Guide 24.2.ea. Page 8 of 1171. Managing Data formats Managing Charts Working with Spatial Geographic Information System (GIS) data GIS examples Working Working with XML and JSON Virtual column expressions Mock Data generation SQL Editor overview SQL Execution Query execution plan Script management SQL Assist and Auto Complete SQL templates Visual

Guide 24.2.ea. Page 7 of 1010. Data Editor overview Data View and Format Data Filters Data viewing and editing Data refresh Navigation Managing Panels Managing Data formats Managing Charts Working Information System (GIS) data GIS examples Working with XML and JSON Virtual column expressions SQL Editor overview Data Editor tools Viewing and editing data Data operations Advanced data types SQL Editor Pending Transactions Auto and manual commit modes Query Manager Data transfer Data import and replace Search overview Data Search Database full text Search Database metadata Search File Search

Confidential—Restricted / 51 THE HYBRID DATA COMPANY We believe that data can make what is impossible today, possible tomorrow We empower people to transform complex data anywhere into actionable insights easier We deliver a hybrid data platform with secure data management and portable cloud-native data analytics / 51 3 Confidential—Restricted Strategic Roadmap for Migrating Data Management to the Cloud G00746011, Analyst(s): Robert Thanaraj, Adam Ronthal, Donald Feinberg “The future data ecosystem should leverage distributed data management components — which may run on multiple clouds and/or on-premises

Knative which can generate specific Knative Service provisioning workload and provides aggregated data of Knative Service ready duration. o Knative Performance Testing Framework 2 Design #IstioCon o included in Istio 1.6.0+ resolved this issue. o Istiod MEM bumped with large numbers of Knative Services (#25532) Mem usage optimization of pilot resolved this issue. • Tune CPU/MEM to ensure enough values o ingress_ready bumped to ~=800 seconds with 500+ Knative Services • Detect and analyze Istio scalability issue 550 Knative Services #IstioCon Istio scalability optimization during Knative Service

Cassandra 1 Create a Keyspace (database) 1 Create a Column Family 2 Insert, Update, Delete, Read Data 2 Getting Started with Cassandra and DataStax Community Edition 2 Installing a Single-Node Instance Application 6 About the Portfolio Demo Use Case 6 Running the Demo Web Application 6 Exploring the Sample Data Model 7 Looking at the Schema Definitions in Cassandra-CLI 8 DataStax Community Release Notes 8 Membership and Seed Nodes 9 About Failure Detection and Recovery 9 About Data Partitioning in Cassandra 10 About Partitioning in Multi-Data Center Clusters 10 Understanding the Partitioner Types 12 About the

Users (GUI/API) Container Schedulers/Runtimes Consumers LDAP/Active Directory Supporting services Harbor Packaging Docker Kubernetes Cloud Foundry 12 Confidential � ©2018 VMware, Inc. SECURITY Tracker § Ubuntu CVE Tracker § Red Hat Security Data § Oracle Linux Security Data § Alpine SecDB API Registry V2 Job Service Console DB Harbor Save Data Pull Layers Scan Get Info Dispatch Jobs �������� § Debian Security Bug Tracker § Ubuntu CVE Tracker § Red Hat Security Data § Oracle Linux Security Data § Alpine SecDB 20 Confidential � ©2018 VMware, Inc. Replication Policy DISTRIBUTION