Internal Combustion Engine Flight Synthetic Fertilizer Transistors PCs Internet Smartphones Cloud12 …Technology Compounding Over Fifty-Plus Years = Better + Faster + Cheaper → More Note: PC units base as of 2010. Mobile internet units are the installed based of smartphones & tablets in 2020. Cloud & data center capex includes Google, Amazon, Microsoft, Meta, Alibaba, Apple, IBM, Oracle, Tencent Trillion Enterprise Impact’ via Morgan Stanley (10/23) Enabling Infrastructure CPUs Big Data / Cloud GPUs Computing Cycles Over Time – 1960s-2020s, per Morgan Stanley Note: Axis is logarithmic;

team refined it over 2 year period and allowed Facebook to handle 6X production load compared to native PHP. d. ENABLE EVERYONE TO TEACH AND LEARN i. Dedicate organizational time to let teaching and tests for appropriate settings and use the tests to scan environments for vulnerabilities iii. 18F Cloud.gov (uses AWS GovCloud – 1. Created platform addressing bulk of compliance concerns driven by ATO AWS – auditors traditional training of sampling with screenshot evidence doesn’t really work in cloud, container or similar environments with infrastructure-as-code and auto- scaling. Must create alternatives

http_client.fetch(url) return response.body And here is the same function rewritten asynchronously as a native coroutine: from tornado.httpclient import AsyncHTTPClient async def asynchronous_fetch(url): http_client 15Tornado Documentation, Release 6.5.1 Native vs decorated coroutines Python 3.5 introduced the async and await keywords (functions using these keywords are also called “native corou- tines”). For compatibility you can use “decorated” or “yield-based” coroutines using the tornado.gen.coroutine decorator. Native coroutines are the recommended form whenever possible. Only use decorated coroutines when compatibility

guide Introduction Asynchronous and non-Blocking I/O Blocking Asynchronous Examples Coroutines Native vs decorated coroutines How it works How to call a coroutine Coroutine patterns Calling blocking fetch(url) return response.body And here is the same function rewritten asynchronously as a native coroutine: from tornado.httpclient import AsyncHTTPClient async def asynchronous_fetch(url): fetch(url) return response.body Native vs decorated coroutines Python 3.5 introduced the async and await keywords (functions using these keywords are also called “native coroutines”). For compatibility

complexity in querying distributed datasets. Horizontal scalability across hybrid environments, supporting cloud, on- prem, and edge deployments. Always-on architecture ensuring availability and resilience as business infrastructure complexity, storage costs, and maintenance overhead by consolidating disparate systems. The native SQL support also makes it simple to use as it provides a single and easy way to query the data.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1559 101.7 High-level Overview of the Native-Code Generation Process . . . . . . . . . . . . . . 1560 101.8 Julia Functions . . . . . . . . Arithmetic, which can handle operations on numeric values that cannot be represented effectively in native hardware representations, but at the cost of relatively slower performance. The following are Julia's Julia also defines the types Int and UInt, which are aliases for the system's signed and unsigned native integer types respectively: # 32-bit system: julia> Int Int32 julia> UInt UInt32 # 64-bit

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1559 101.7 High-level Overview of the Native-Code Generation Process . . . . . . . . . . . . . . 1560 101.8 Julia Functions . . . . . . . . Arithmetic, which can handle operations on numeric values that cannot be represented effectively in native hardware representations, but at the cost of relatively slower performance. The following are Julia's Julia also defines the types Int and UInt, which are aliases for the system's signed and unsigned native integer types respectively: # 32-bit system: julia> Int Int32 julia> UInt UInt32 # 64-bit

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1840 105.7 High-level Overview of the Native-Code Generation Process . . . . . . . . . . . . . . 1841 105.8 Julia Functions . . . . . . . . Arithmetic, which can handle operations on numeric values that cannot be represented effectively in native hardware representations, but at the cost of relatively slower performance. The following are Julia's Julia also defines the types Int and UInt, which are aliases for the system's signed and unsigned native integer types respectively: # 32-bit system: julia> Int Int32CHAPTER 5. INTEGERS AND FLOATING-POINT

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1840 105.7 High-level Overview of the Native-Code Generation Process . . . . . . . . . . . . . . 1841 105.8 Julia Functions . . . . . . . . Arithmetic, which can handle operations on numeric values that cannot be represented effectively in native hardware representations, but at the cost of relatively slower performance. The following are Julia's Julia also defines the types Int and UInt, which are aliases for the system's signed and unsigned native integer types respectively: # 32-bit system: julia> Int Int32CHAPTER 5. INTEGERS AND FLOATING-POINT

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1840 105.7 High-level Overview of the Native-Code Generation Process . . . . . . . . . . . . . . 1841 105.8 Julia Functions . . . . . . . . Arithmetic, which can handle operations on numeric values that cannot be represented effectively in native hardware representations, but at the cost of relatively slower performance. The following are Julia's Julia also defines the types Int and UInt, which are aliases for the system's signed and unsigned native integer types respectively: # 32-bit system: julia> Int Int32CHAPTER 5. INTEGERS AND FLOATING-POINT