This document summarizes a presentation about using Apache Ignite as a distributed database. It discusses the limitations of standard RDBMS and NoSQL databases, and how Ignite addresses these by providing a consistent and scalable solution. The presentation outlines Ignite's features and architecture, and shares reference cases from companies like ING Group and Ping An Insurance that have benefited from using Ignite. It concludes by providing additional Ignite learning resources and information on downloading a free trial.

1. Getting Started with Apache Ignite as
a Distributed Database
Stephen Leung
Director of Solution Architect, Asia Pacific
2019 © GridGain Systems

2. 2018 © GridGain Systems
Agenda
• Why not standard RDBMS and NoSQL?
• Apache Ignite Overview
• Reference Case sharing
• Q & A

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Why not Standard RDBMS and NoSQL?

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Existing Databases are Too Slow

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Existing Databases Can't Scale

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Existing Databases Can't Scale

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NoSQL to the Rescue?

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NoSQL to the Rescue?
RDBMS
Consistent,
but
not scalable
NoSQL
Scalable, but
not consistent

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Apache Ignite
Consistent &
Scalable

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Ignite
Data Warehouse Operational DB
TransactionsAnalytics, ML, AI
ETL
Analytics, ML, AI & Transactions
Real Time, Scalable, Available, Flexible
ETL, Batch, Inflexible
HTAP Architecture

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In-Memory Computing
Mainframe NoSQL Hadoop
Data Layer
RDBMS
In-Memory Computing
Financial Services Initiatives
Private/Public
Cloud/SaaS
Regulatory
Compliance
Omnichannel
Banking
Security
Fraud
Risk
Management
Trading
HPC

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Current solution comparison
Feature
In-Memory Cache
(Redis)
IMDG
(Hazelcast, GigaSpaces)
IMDB
(MemSQL, VoltDB)
IMC Platform
(GridGain)
Scale Out and Availability ✓ ✓ ✓ ✓
In-Memory ✓ ✓ ✓ ✓
Complements 3rd Party DB X ✓ X ✓
Persistence as Extension ✓ X
(copy of data in RAM)
X
(snapshots)
✓
Fast Restarts X X X ✓
Consistency and Transactions X ✓ ✓ ✓
SQL X X ✓ ✓
Collocated Processing X ✓ X ✓
Machine and Deep Learning ✓ X X ✓

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Apache Ignite Overview

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Memory-centric distributed
database, caching, and processing platform
In-memory computing platform based Apache Ignite
adding enterprise features and support
for mission critical deployments

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15
Among Top Apache Projects
Top 5 Developer
Mailing Lists
1. Beam
2. Ignite
3. Kafka
4. Tomcat
5. James
Top 5 User
Mailing Lists
1. Flink
2. Lucene
3. Ignite
4. Cassandra
5. Kafka
Over 2M downloads per year

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In-Memory Computing Benefits
In-Memory Speeds
10-1,000x faster than systems built on disk-based databases
Massive Scalability
Scale out to petabytes of in-memory data
Easy to Implement
No rip-and-replace of existing databases

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Comprehensive Solution
• Slides In Between Existing
Application and Data Layers
• Works With RDBMS, NoSQL and
Hadoop Databases
• Multi-Language Support Including
SQL, Java, .NET, PHP, Node.js, Scala
and MapReduce
• Deploy On-Premises, In the Cloud, or
on Hybrid Environments

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eCommerce, Retail & Travel
Financial Services
Software
FinTech Pharma & Healthcare
Ignite – Used by Leading Companies Worldwide
IoT AdTech
Telecom & Mobile
Logistics & Transportation

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Apache Ignite In-Memory Computing Platform
Security&Auditing
Monitoring&Management
DataSnapshots&Recovery
Memory-Centric Storage
Scale to 1000s of Nodes & Store TBs of Data
Ignite Native Persistence
(Flash, SSD, Intel 3D XPoint)
Third-Party Persistence
Keep Your Own DB
(RDBMS, HDFS, NoSQL)
SQL Transactions Compute Services MLStreamingKey/Value
IoTFinancial
Services
Pharma &
Healthcare
E-CommerceTravel &
Logistics
Telco
DataCenterReplication

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Distributed In-Memory Data Store
In-Memory Data Store
GridGain Server Cluster
Predictable Memory
Consumption
Fully Transactional WAL
(Write Ahead Log)
Instantaneous
Restarts
Automatic
Defragmentation
Off-heap Removes
Noticeable GC Pauses
Stores Superset
of Data
Distributed Persistent Store
In-Memory Data Store
Persistent Store
Server Node
In-Memory Data Store
Persistent Store
Server Node
In-Memory Data Store
Persistent Store
Server Node

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Reference Case Sharing

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The ING Group is a Dutch multinational banking
and financial services corporation headquartered
in Amsterdam. Its primary businesses are retail
banking, direct banking, commercial banking, investment
banking, asset management, and insurance services.
Problem
• To deliver new competitive customer services fast
• High cost of running on mainframe infrastructure
• Transaction consistency over multiple geo-locations
GridGain Solution
• Powers the core solution for delivering new services
Data aggregation across multiple sources
• Reduces infrastructure costs
Front-End APIs
Payments SecuritiesAccounts Credits Clients
Multi-datacenter Infrastructure
GridGain In-Memory Computing Platform
- Next Generation Banking

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Wellington - Next Generation, Real-time IBOR
A top 20 worldwide asset management firm
with over $1 trillion under management
• Problem
– Current systems no longer scaled to handle the volumes
– Didn’t comply with new regulations following financial crisis
– Needed to introduce new asset classes faster
• GridGain Solution
– Investment Book of Record (IBOR), a single real-time
version of the truth for positions, exposure, valuations
and performance for all customers, teams and trades.
– 10x performance gains, linear horizontal scalability
– Support for SQL and ACID transactions, and for
existing systems and skillsets
– Enabled transactions and analytics on a single platform
– Collocated computing scales complex calculations, analytics
Trading
Systems
GridGain In-Memory Computing Platform
In-Memory
Data Grid
In-Memory
Database
Streaming
Analytics
Continuous
Learning Framework
Accounting
System
Other
Back Office
Portfolio
Management
Risk
Management
Regulatory &
Compliance
Investment
Book of Record
(IBOR)
Oracle RAC

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Ping An – Accelerate the database performance
24
Ping An Insurance has over 1M sales people
generating US$122.4B+ in revenue
Front-End APIs
SQL TXCompute
Service
Grid
Key /
Value
Commission Calculation System
GridGain In-Memory Computing Platform
Distributed In-Memory Data Store
Distributed Persistent Store
In-Memory Data Store
Persistent Store
Server Node
In-Memory Data Store
Persistent Store
Server Node
In-Memory Data Store
Persistent Store
Server Node
Problems
• Calculating sales commissions is a complex matrix math
challenge
• Key requirements: accuracy and speed
• Current platform: Oracle HW+SW for multi-field/range-
based queries across 8 or more tables
• Goal: achieve 5x improvement in calculation times—
without errors
GridGain Solution
• Reduce cost
– Move out from Oracle to GridGain
• Improve the delivery performance
– the commission calculation change from 8 hours to 1 hour.
• Immediate restart from disk (no memory warm-up)

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What is Ignite?
- https://apacheignite.readme.io/docs/what-is-ignite
Getting Start
- https://apacheignite.readme.io/docs/getting-started
- In-memory computing essentials: Part 1
- In-memory computing essentials: Part 2
Download Apache Ignite
- https://ignite.apache.org/download.cgi
Free 30-Day Ultimate, Enterprise or Professional
Edition Trial
- https://www.gridgain.com/resources/download
Next Steps or Any Questions?