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Future of The DBA DevOps, the Cloud Paradigm, & the Microsoft Data Platform Stuart R Ainsworth
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Agenda 1 2 3 4 Describe the typical state of database administration
Define & describe DevOps and the cloud computing paradigm 2 Explore (high-level) the Microsoft Data Platform 3 Discuss the implications for data professionals 4 Agenda
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YOU ME YOU SQL Server experience
Exposure to database admin & architecture Learning-centered Desire to build modern skills IT Mgr [REDACTED] Consultant & Contractor Former Data Architect, DBA, developer AtlantaMDF Chapter Leader Infrequent blogger: My career trajectory
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What’s All This? Workflow & Database Professionals
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Workflow Software Development Life Cycle
Who’s responsible for what, when. Encompasses development, testing, & operations Org Chart usually reflects workflow
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Typical SQL Server Person
Wears multiple hats Develops SQL and runs backups Smaller organizations Unites the Realm
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Typical SQL Server Person
“NOT” a developer “Fixes” SQL problems Usually reports to Operations Says NI! (NO) a lot
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Risk of Tribal Knowledge Risk of Burnout Separation of Duties
Smaller Teams Enterprise Teams Limited Resources Risk of Tribal Knowledge Risk of Burnout Separation of Duties Risk of Turf Wars Risk of Software Drift
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Typical SQL Server Person
Development Skills Administration Skills SQL (DDL & DML) Performance Tuning (Code) Index Analysis Data Warehousing Reporting Server Configuration Performance Tuning (Server) Index Maintenance Backups and Restores Security
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MCSA SQL 2012\2014 70-461: Querying Microsoft SQL Server 2012/2014
70-462: Administering Microsoft SQL Server 2012/2014 Databases 70-463: Implementing a Data Warehouse with Microsoft SQL Server 2012/2014
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What’s coming… Data production is accelerating Data is diversifying
Est .79ZB in 2009 Est 7.9ZB in 2015 Est 35ZB in 2020 (44 times greater than 2009) Data is diversifying Relational Data Big (Size) Data Fast Data Dark Data Lost Data New Data
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DevOps Philosophy, not Methodology
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A (Very) Brief Overview
DevOps is focused on delivering quality, faster. Philosophical approach, not methodological Automation, infrastructure as code, continuous deployment Emphasis on communication; silo reduction Born out of Agile, several innovators contributing Patrick Debois & Andrew Clay Shafer – Agile Infrastructure (Agile 08) John Allspaw & Paul Hammond – 10+ Deploys Per Day (Velocity 09) Gene Kim, Kevin Behr, & George Spafford – The Phoenix Project
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The Phoenix Project
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The Phoenix Project AMPLIFY – make louder
Get Feedback into the hands of development as soon as possible, in form of stated issues. AUTOMATE YOUR FEEDBACK SYSTEMS AS MUCH AS POSSIBLE. Data is Data; start looking at feedback as a data challenge to solve. Note that the arrows are circular; if you can speed up the deployment time, and the feedback time, it becomes much easier to move forward and not rollback.
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The Phoenix Project IF you get your deployment and feedback cycles tight, and eliminate rollbacks, that gives you the opportunity to take risks and expirmenet. Get new features into the hands of beta customers, faster, for example If your application monitoring systems tell you it’s breaking, then you can quickly deploy a fix.
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(Some) Components of DevOps
Automation Source Control Continuous Integration & Builds Configuration Management Infrastructure as Code Automatic Monitoring Firefighting Playbooks, Workflow, Incident Management
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Unicorns, Horses, and Mules
Unicorns are sparkly, magical companies that do amazing things with DevOps Horses are the typical enterprise; strong in some areas, always looking to improve. Mules are conservative; slow and steady, reluctant to change. There is no “right” way, but there are some good ideas.
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Key Takeaways Every organization does DevOps differently
DevOps is rooted in a sense of continuous improvement People over tools Reduce silos by focusing on shared goals, not technology Technology spans function Goals fulfill function; method matters less In simplified terms, this is the way most deployments SHOULD work. However, the more complicated the deployment (the more moving parts, more types), the more likely it is that the deployment will fail, and then you face the dreaded ROLLBACK For example, say you have a .NET web app that connects to a database; when you make a change to the app (system engineers), you also need to deploy some new stored procedures (DBA’s) The simpler the deployment, the less likely the need to rollback.
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The Cloud Paradigm Infrastructure, Platform, Software
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What do we mean by “The Cloud”?
Trendy marketing term? Network hosting? Internet connected services? Distributed, scalable, shared computing resources
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Product-Focused Paradigm
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Cloud Paradigm Applications Data Runtime Middleware O/S Virtualization
Servers Storage Networking
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Cloud Paradigm
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Cloud Paradigm
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Limoncelli, Chalup, Hogan
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Ideal System Architecture
Scalable Resilient to Failure (Redundant) Service-Oriented Architecture Automated Monitoring, Configuration and Build “Infrastructure As Code”
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Ideal Release Process Completely Automatic
Code checked in -> new build Unit & regression testing User acceptance testing Continuous Integration Dependent on “infrastructure as code” Micro-releases (100 deployments per day) No rollbacks
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Ideal Operations Automatic instrumentation (Logging)
Long Term Storage Predictive Analytics Automatic Error Logging & Alerting Respond to Every Error On-call Rotation includes Developers Automatic Scaling Scale Up Scale Down “Zero Maintenance”
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Ideal Data Architecture
CAP Principle (Gilbert & Lynch) Consistency - all nodes are guaranteed to see same data Availability – every request receives feedback for success/failure Partition Tolerant – system operates despite loss of part of system At any one time, any two attributes are achievable in combination, but not all three at the same time.
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CAP Principle Consistent Available Partition Tolerant
SQL Server Relational Engine Hadoop Available Cassandra Partition Tolerant Consistency - all nodes are guaranteed to see same data Availability – every request receives feedback for success/failure Partition Tolerant – system operates despite loss of part of system
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Microsoft Data Platform
Diversity in Data
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Microsoft Data Platform
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Microsoft Data Platform
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Microsoft Data Platform
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Impact on Careers Future Prognostications
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Current Trends Companies are recognizing the value of different kinds of data, and an increased need for analytics. Adoption of Big Data technologies is on the rise Data Science jobs are increasing The Internet (or Fog) of Things is coming. Operational methodologies like Agile and DevOps are pushing companies toward the Cloud Paradigm.
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Current Trends The Cloud Paradigm with its separation of duties is causing companies to realign resources. Infrastructure Teams Platform Teams Software Teams Operational technologies (virtualization, scripting) are allowing organizations to scale out computing resources with fewer human resources.
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Cloud Paradigm SQL SERVER Applications Data Runtime Middleware O/S
Virtualization Servers Storage Networking SOFTWARE SQL SERVER PLATFORM INFRA STRUCTURE
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My Predictions Increased segregation (& communication) between Dev & Ops roles. Number of development jobs will increase Diversity of data platform. Need for integration. Data mining and analysis skills. Number of administrative jobs will decrease Infrastructure as code, scripting, virtualization Product specific specialists for initial configuration
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What does this mean for YOU?
Choose your path: Development or Administration Developers have opportunities for breadth: Big Data (Hadoop, HDInsight) Data Science (Statistics, R) Visualizations (Reporting, Power BI) Administrators have opportunities for depth: Always On Infrastructure & Platform impacts Scripting & configurations
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MCSA SQL 2016 Querying Data with Transact-SQL Developing SQL Databases
Administering a SQL Database Infrastructure Provisioning SQL Databases Implementing a SQL Data Warehouse Developing SQL Models
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Stuart R. Ainsworth Blog: Contact Me
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