Unified Data Layers: A New Era for Scalable Analytics, Search, and AI v 1.1Table of Contents Introduction 1. The Interconnection of Analytics, Search, and AI 2. What is a Real-Time Unified Data Layer Layer? 3. Why Do You Need a Real-Time Unified Data Layer? 4. 5.CrateDB: A Modern Real-Time Unified Data Layer1. Introduction Data teams are facing more challenges than ever. As applications generate and and consume unprecedented volumes of data across a growing number of sources and formats, data engineering and architecture teams must design systems that not only scale but also deliver real-time access

datapoints turned into this beast. As soon as we updated one chart, we often had to update another – a data game of whack-a-mole… a pattern that shows no sign of stopping…and will grow more complex as competition related to the artificial intelligence technology evolution is indeed unprecedented, as supported by the data. This document is filled with user, usage and revenue charts that go up-and-to-the-right… often supported Threats = Rising Competition + Open-Source Momentum + China’s Rise • AI & Physical World Ramps = Fast + Data-Driven • Global Internet User Ramps Powered by AI from Get-Go = Growth We Have Not Seen Likes of

small-medium-large as units for assigning story points. Over time, as the teams accumulate performance data, this iterative and incremental4 process improves accuracy in allocating points. Point values are typically is the contractor team of software developers, including software and security engineers, data specialists, testers, quality assurance, and configuration managers. Ideally these participants capability standpoint must also be flexible.22 Figure 13: Example of Requirements Documentation (Data Source: JCIDS Manual) 8.1.2 Aligning IT Box Requirements to Agile Development The IS ICD provides

a. Goal – Enable & sustain fast flow of work by implementing continuous delivery i. Create the foundation of our deployment pipeline ii. Enabling fast & reliable automated testing iii. Enabling & practicing Operations to improve outcomes 2. Ch. 9 – Create the Foundations of Our Deployment Pipeline a. Enterprise Data Warehouse program by Em Campbell-Pretty - $200M, All streams of work were significantly behind schedule Application code & dependencies 2. Environment scripts & creation tools 3. DB scripts and reference data 4. Containers 5. Automated tests 6. Project artifacts – documentation, procedures, etc. 7. Application

 DO epics and user stories to concisely define the desired system functions and provide the foundation for Agile estimation and planning. o They describe what the users want to accomplish with the

25.2 Starting and managing worker processes . . . . . . . . . . . . . . . . . . . . . . . 314 25.3 Data Movement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315 25.4 Global Global Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 27.15 Accessing Data through a Pointer . . . . . . . . . . . . . . . . . . . . . . . . . . . 362 27.16 Thread-safety using views for slices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435 34.17 Copying data is not always bad . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435 34.18 Consider StaticArrays

25.2 Starting and managing worker processes . . . . . . . . . . . . . . . . . . . . . . . 314 25.3 Data Movement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315 25.4 Global Global Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 27.15 Accessing Data through a Pointer . . . . . . . . . . . . . . . . . . . . . . . . . . . 362 27.16 Thread-safety using views for slices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435 34.17 Copying data is not always bad . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435 34.18 Consider StaticArrays

Macro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321 25.4 Communication and data-races between threads . . . . . . . . . . . . . . . . . . . 323 25.5 Side effects and mutable function 26.2 Starting and managing worker processes . . . . . . . . . . . . . . . . . . . . . . . 333 26.3 Data Movement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334 26.4 Global Global Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 28.15 Accessing Data through a Pointer . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 28.16 Thread-safety

Macro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321 25.4 Communication and data-races between threads . . . . . . . . . . . . . . . . . . . 323 25.5 Side effects and mutable function 26.2 Starting and managing worker processes . . . . . . . . . . . . . . . . . . . . . . . 333 26.3 Data Movement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334 26.4 Global Global Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 28.15 Accessing Data through a Pointer . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 28.16 Thread-safety

Macro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321 25.4 Communication and data-races between threads . . . . . . . . . . . . . . . . . . . 323 25.5 Side effects and mutable function 26.2 Starting and managing worker processes . . . . . . . . . . . . . . . . . . . . . . . 333 26.3 Data Movement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334 26.4 Global Global Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383 28.15 Accessing Data through a Pointer . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 28.16 Thread-safety