real-time streaming and analytics at scale with Apache Kafka and Apache Ignite June, 2019 / New York, NY @denismagda | @gamussa | #NYCInMemory

2 Hello 👋 @gamussa @denismagda @denismagda | @gamussa | #NYCInMemory

Digital transformation challenges @denismagda | @gamussa | #NYCInMemory

4 Digital Transformations Challenges Application Layer Web-Scale Apps IoT 10-100x Queries and Transactions (per sec) Mobile Apps Social Media 10-1000x Faster Analytics (Hours to Sec) 50x Data Storage (Big Data) Data Layer NoSQL RDBMS @denismagda | @gamussa Hadoop | #NYCInMemory

4 Digital Transformations Challenges Application Layer ● 10-100x more queries and transactions Web-Scale Apps IoT 10-100x Queries and Transactions (per sec) Mobile Apps Social Media 10-1000x Faster Analytics (Hours to Sec) 50x Data Storage (Big Data) Data Layer NoSQL RDBMS @denismagda | @gamussa Hadoop | #NYCInMemory

4 Digital Transformations Challenges Application Layer ● 10-100x more queries and transactions ● 50x more data today as a decade ago Web-Scale Apps IoT 10-100x Queries and Transactions (per sec) Mobile Apps Social Media 10-1000x Faster Analytics (Hours to Sec) 50x Data Storage (Big Data) Data Layer NoSQL RDBMS @denismagda | @gamussa Hadoop | #NYCInMemory

4 Digital Transformations Challenges Application Layer ● 10-100x more queries and transactions ● 50x more data today as a decade ago ● Overnight analytics become real-time Web-Scale Apps IoT 10-100x Queries and Transactions (per sec) Mobile Apps Social Media 10-1000x Faster Analytics (Hours to Sec) 50x Data Storage (Big Data) Data Layer NoSQL RDBMS @denismagda | @gamussa Hadoop | #NYCInMemory

5 @denismagda | @gamussa | #NYCInMemory

5 @denismagda | @gamussa | #NYCInMemory

5 @denismagda | @gamussa | #NYCInMemory

5 @denismagda | @gamussa | #NYCInMemory

5 @denismagda | @gamussa | #NYCInMemory

5 @denismagda | @gamussa | #NYCInMemory

5 @denismagda | @gamussa | #NYCInMemory

In-Memory Computing and Stream processing Application Layer Web-Scale Apps IoT Mobile Apps Social Media Confluent Platform GridGain In-Memory Computing Platform Event Streaming Transactional Persistence @denismagda | @gamussa | #NYCInMemory

In-Memory Computing and Stream processing • Performance and velocity increases Application Layer Web-Scale Apps IoT Mobile Apps Social Media Confluent Platform GridGain In-Memory Computing Platform Event Streaming Transactional Persistence @denismagda | @gamussa | #NYCInMemory

In-Memory Computing and Stream processing • Performance and velocity increases Application Layer Web-Scale Apps IoT Mobile Apps Social Media • Scalability up to petabytes of data Confluent Platform GridGain In-Memory Computing Platform Event Streaming Transactional Persistence @denismagda | @gamussa | #NYCInMemory

In-Memory Computing and Stream processing • Performance and velocity increases Application Layer Web-Scale Apps IoT Mobile Apps Social Media • Scalability up to petabytes of data • Act faster by analyzing streams of data Confluent Platform GridGain In-Memory Computing Platform Event Streaming Transactional Persistence using SQL language @denismagda | @gamussa | #NYCInMemory

8 Streaming-First Workd @denismagda | @gamussa | #NYCInMemory

9 Kappa Architecture: GridGain and Kafka Connect 💵 @denismagda | @gamussa | #NYCInMemory

Demo @denismagda | @gamussa | #NYCInMemory

Enter Kafka Connect @denismagda | @gamussa | #NYCInMemory

13 @denismagda | @gamussa | #NYCInMemory

13 @denismagda PRODUCER Producer Application | @gamussa | #NYCInMemory

13 CONSUMER @denismagda PRODUCER Producer Application | @gamussa | Consumer Application #NYCInMemory

14 KAFKA CONNECT KAFKA CONNECT CONSUMER PRODUCER @denismagda | @gamussa | #NYCInMemory

14 KAFKA CONNECT KAFKA CONNECT CONSUMER PRODUCER Source Connector SMTs Converter @denismagda | @gamussa | #NYCInMemory

14 KAFKA CONNECT KAFKA CONNECT CONSUMER PRODUCER Source Connector SMTs Converter SMTs Converter @denismagda | @gamussa | Sink Connector #NYCInMemory

15 Discover connectors, SMTs, and converters @denismagda | @gamussa | #NYCInMemory

16 Discover connectors, SMTs, and converters Descriptions, licensing, support, and more @denismagda | @gamussa | #NYCInMemory

17 Lower the Bar to Enter the World Coding Sophistication Core developers who use Java/Scala streams Core developers who don’t use Java/Scala Data engineers, architects, DevOps/SRE BI analysts User Population @denismagda | @gamussa | #NYCInMemory

17 Lower the Bar to Enter the World Coding Sophistication Core developers who use Java/Scala streams Core developers who don’t use Java/Scala Data engineers, architects, DevOps/SRE BI analysts User Population @denismagda | @gamussa | #NYCInMemory

Store and process with GridGain @denismagda | @gamussa | #NYCInMemory

GridGain: Real-time Streaming and Analytics @denismagda | @gamussa | #NYCInMemory 19

20 Essential GridGain APIs Distributed memory-centric storage Co-located Computations Combines the performance and scale of inmemory computing together with the disk durability and strong consistency in one system Brings the computations to the servers where the data actually resides, eliminating need to move data over the network Distributed SQL Horizontally, fault-tolerant distributed SQL database that treats memory and disk as active storage tiers Distributed Key-Value Read, write and transact with fast key-value APIs ACID Transactions Machine and Deep Learning Supports distributed ACID transactions for key-value as well as SQL operations Set of simple, scalable and efficient tools that allow building predictive machine learning models without costly data transfers (ETL) @denismagda | @gamussa | #NYCInMemory

21 GridGain SQL For Real-Time Analytics Ignite Node Toronto 2 Canada Montreal Ottawa Calgary 1 Ignite Node 3 2 India New Delhi

  1. Initial Query 2. Query execution over local data 3. Reduce multiple results in one @denismagda Mumbai | @gamussa | #NYCInMemory

Thanks! @denismagda dmagda@gridgain.com @gamussa viktor@confluent.io @denismagda | @ @gamussa | #NYCInMemory

Q&A @denismagda | @gamussa | #NYCInMemory