Scaling Cloud Web & Data Technologies

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www.anant.us | solutions@anant.us | 202.905.2818
1010 Wisconsin Ave, NW | Suite 250 | Washington, DC 20007
Streamline Organize Unify
Business Information
Scaling Cloud Web & Data Technologies
Distributed Platform:
Spark, Cassandra, Kafka, Docker

• Who are these guys? (Intro)
• Why Distribute Everything?
• Spark: Distributed Computing
• Cassandra: Distributed Data
• Kafka: Distributed Queues
• Docker: Simplified Code Distribution
• Questions ?
Agenda

Who am I. What I did. What I do.
Built a Hosting Company
Web Application Dev/Hosting
IT Consulting w/ Firms/Agencies
……………………………………...……...
Management Consulting for
Internet Teams + Difficult Data

What we do well.
Streamline, Organize & Unify
Business Information

What is Business Information
Internal vs. External (Public vs. Private)
Strategic / Tactical / Operational
Data / Information / Knowledge
Documents / Records / Metrics
Project
Information
Client Service
Information
Corporate
Guides
Collaborative
Documents
Assets
& Files
Corporate
Assets

Phases of Business Technology
Business
Silos
Standardize
d Platform
Optimized
Core
Business
Modularity

Your Business Cloud is Disconnected
… so you and your team spend waste time ...

Bring it all together in One Place
Project
Information
Client Service
Information
Corporate
Guides
Collaborative
Documents
Assets
& Files
Corporate
Assets
Konotree
We will help you connect
and organize your data.

find. analyze. act.
FIND
DISCOVER
FILTER
ANALYZE
VISUALIZE
MEASURE
ACT
USE
SHARE

Why Distribute Everything?
• Pros
– Scale
– Speed
– Concurrency
– Volume
– Modularity
• Cons
– Complexity
– Latency
– Parity

Systems Amateurity
• Gonna host a ..:
– Project Management System ?
– Chat Server ?
– Frontend / Backend Framework ?
– Blog System / CMS / eCommerce ?
– Social Network ?

Systems Amateurity
• Equivalent to restaurants making their own
pots and pans
• Common in many teams - small to large
• “I can SELF-HOST EVERYTHING”

Kafka
• 20-30 Times More Throughput
• PubSub or Message Queue
• Persistent w/ Offset (Replays)
• Messaging / ESB / Integration
• Seen in real world ~ 15TB / Day

Spark
• Extremely Fast Processing
• Program in Java, Scala, Python, R, or C#
• Sophisticated Algorithms Available: PageRank in 5 lines.
• Real Time Stream Processing
• Awesome Integration with Hadoop or Other Data Sources

Cassandra
• Massive Amounts of Data Quickly
• Distributed Architecture - Master - Master
• Tunable Consistency - CAP
• CQL ~ SQL - Familiar / Similar Syntax
• Easily Scale DB with Additional Nodes
• Easily add Replicas in additional DCs
• Hybrid of Schema / Schema-less
• Low Barrier to Entry

Docker
• Derivable Images
• Immutable Containers
• Same Docker works Everywhere
• Same Compose works Everywhere
• Industry Wide Adoption
• Thousands of Docker Images Available
• Thousands of Compose Files Available
• Runs on Linux or Windows (Natively)
• Tested with * on Linux
• Tested with DotNet core on Linux
• Tested with Legacy .NET/MSSQL on Windows

Ongoing Subscription + Kickstart
KICKSTART*
- Discovery*
- Workshop
- Architecture*
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- Engineering*
- Delivery
SUBSCRIPTION
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- Advisory / Guidance
- Maintenance*
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*service optionsFree Consultation

www.anant.us | solutions@anant.us | 202.905.2818
1010 Wisconsin Ave, NW | Suite 250 | Washington, DC 20007
Streamlined Data
Integration / Data Pipelines
Organized Knowledge
Search / Data Warehouses
Unified Interfaces
Portals / Dashboards / Mobile
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Anant Corporation

In Apache Cassandra Lunch #119, Rahul Singh will cover a refresher on GUI desktop/web tools for users that want to get their hands dirty with Cassandra but don't want to deal with CQLSH to do simple queries. Some of the tools are web-based and others are installed on your desktop. Since the beginning days of Cassandra, a lot has changed and there are many options for command-line-haters to use Cassandra.

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This document discusses automating Apache Cassandra operations using Apache Airflow. It recommends using Airflow to schedule and automate workflows for ETL, data hygiene, import/export, and more. It provides an overview of using Apache Spark jobs within Airflow DAGs to perform tasks like data cleaning, deduplication, and migrations for Cassandra. The document includes demos of using Airflow and Spark with Cassandra on DataStax Astra and discusses considerations for implementing this solution.

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1. www.anant.us | solutions@anant.us | 202.905.2818 1010 Wisconsin Ave, NW | Suite 250 | Washington, DC 20007 Streamline Organize Unify Business Information Scaling Cloud Web & Data Technologies Distributed Platform: Spark, Cassandra, Kafka, Docker

2. • Who are these guys? (Intro) • Why Distribute Everything? • Spark: Distributed Computing • Cassandra: Distributed Data • Kafka: Distributed Queues • Docker: Simplified Code Distribution • Questions ? Agenda

3. Who am I. What I did. What I do. Built a Hosting Company Web Application Dev/Hosting IT Consulting w/ Firms/Agencies ……………………………………...……... Management Consulting for Internet Teams + Difficult Data

4. What we do well. Streamline, Organize & Unify Business Information

5. What is Business Information Internal vs. External (Public vs. Private) Strategic / Tactical / Operational Data / Information / Knowledge Documents / Records / Metrics Project Information Client Service Information Corporate Guides Collaborative Documents Assets & Files Corporate Assets

6. Phases of Business Technology Business Silos Standardize d Platform Optimized Core Business Modularity

7. Your Business Cloud is Disconnected … so you and your team spend waste time ...

8. Bring it all together in One Place Project Information Client Service Information Corporate Guides Collaborative Documents Assets & Files Corporate Assets Konotree We will help you connect and organize your data.

9. find. analyze. act. FIND DISCOVER FILTER ANALYZE VISUALIZE MEASURE ACT USE SHARE

10. Why Distribute Everything? • Pros – Scale – Speed – Concurrency – Volume – Modularity • Cons – Complexity – Latency – Parity

11. Systems Amateurity • Gonna host a ..: – Project Management System ? – Chat Server ? – Frontend / Backend Framework ? – Blog System / CMS / eCommerce ? – Social Network ?

12. Systems Amateurity • Equivalent to restaurants making their own pots and pans • Common in many teams - small to large • “I can SELF-HOST EVERYTHING”

13. Kafka • 20-30 Times More Throughput • PubSub or Message Queue • Persistent w/ Offset (Replays) • Messaging / ESB / Integration • Seen in real world ~ 15TB / Day

14. Spark • Extremely Fast Processing • Program in Java, Scala, Python, R, or C# • Sophisticated Algorithms Available: PageRank in 5 lines. • Real Time Stream Processing • Awesome Integration with Hadoop or Other Data Sources

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16. Docker • Derivable Images • Immutable Containers • Same Docker works Everywhere • Same Compose works Everywhere • Industry Wide Adoption • Thousands of Docker Images Available • Thousands of Compose Files Available • Runs on Linux or Windows (Natively) • Tested with * on Linux • Tested with DotNet core on Linux • Tested with Legacy .NET/MSSQL on Windows

17. Docker Makes it Easy to Self Host

18. Docker Makes it Easy to Self Host

19. Docker Makes it Easy to Self Host

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