Data lakes & data warehouses, whether on-premises or in the cloud promise to provide a centralized, cost-effective and scalable foundation for modern analytics. However, organisations continue to struggle to deliver accurate, current and analytics-ready data sets in a timely fashion. Traditional ingestion tools weren’t designed to handle hundreds or even thousands of data sources and the lack of lineage forces data consumers to manually aggregate information from sources they trust. In this session, you’ll learn how to future-proof your modern data environment to meet the needs of the business for the long term. We'll examine how to overcome common challenges, the related must-have technology solutions in the data lake/ data warehousing world, using real-world success stories and even a few architecture tips from industry experts.

1. MODERNIZE AND AUTOMATE
DATA INTEGRATION

2. 2© 2019 Attunity 2
Leader in change data capture
Most comprehensive platform
supportability matrix
Modern architecture & UI
End-to-end offering captures
changed data and delivers
analytics ready data structures
Leader in cloud integration and
cloud migration solutions
Relied on by the top cloud
computing providers
Migrated and integrated more
than 200,000 databases and
mainframes to AWS, Azure and
Google
Complete cloud data delivery
capabilities, including the last
mile
Leader in ease of use and
automation
Automated target table creation
and data instantiation
Automated source and target
mappings
Quickly create and deploy
analytics ready structures
The right architecture for efficiently capturing large volumes of changed data
from heterogeneous source systems and delivering it in real-time to
Streaming & Cloud Platforms, Data Warehouses and Data Lakes
ATTUNITY – MODERNIZE & AUTOMATE DATA
INTEGRATION

3. 3© 2019 Attunity 3© 2019 Attunity
ATTUNITY – MODERNIZE & AUTOMATE
DATA INTEGRATION
1. Real-time – Architected for real-time changed data capture and analytics ready data delivery
2. Heterogeneous – Seamlessly move real-time data between heterogeneous Relational, Mainframe, Big
Data, Datawarehouse, Cloud Platforms, File Systems and Applications with one easy to use solution
3. Complete – End to end automation includes target table creation, initial instantiation, mappings, schema
synchronization, Data Warehouse/Data Mart and Data Lake creation, management and documentation
4. Independence – No hidden agenda, the right business/technical partnerships, more than 2,000
customers (including half of the F100), publicly traded (Nasdaq: ATTU), a partner customers can count
on

4. 4© 2019 Attunity 4© 2019 Attunity
Analyze a broader set of data structures
as well as structured data
Faster and improved decision making
Leverage AI/ML, IoT and decision
automation for a competitive advantage
Requires managed Data Lake creation
and Big Data processing at scale
Requires real-time data from on-
premise systems and cloud platforms
Next Generation Analytics
Reduce the costs associated with legacy
EDW’s and provide elasticity
Meet new business requirements
Support more advanced analytics
Replace traditional ETL with modern
self-service capabilities
Requires real-time data from on-
premise systems and cloud platforms
Data Warehouse Modernization
Legacy application modernization
Faster, easier new application
development
Higher scalability/elasticity
Infrastructure and maintenance cost
savings
Requires real-time data from
on-premise systems
Cloud Application Development
SaaS
IaaS
PaaS
DB
MF
EDW
FILES
DWaaS
TRENDS DRIVING DATA INTEGRATION MODERNIZATION
& AUTOMATION
DATA
CONSUMPTION
& ANALYTICS
DB
MF
EDW
FILES
IaaS
PaaS
DB
MF
EDW
FILES
Micro
Services

5. 5© 2019 Attunity
ATTUNITY – MODERNIZE & AUTOMATE
DATA INTEGRATION
MAINFRAME
SAP
SAAS
APPS
FILES
DATA WAREHOUSE
RDBMS
STREAMING
DATA PIPELINE
AUTOMATION
DESIGN & MANAGE
GENERATE DELIVER REFINE/MERGE
change
stream
To cloud,
lakes
for analytic
use
DATA WAREHOUSES (ON-PREMISES & CLOUD)
Azure SQL
DW
Amazon Redshift
MODEL
OTHER…
OTHER…
DATA LAKES, STREAMING (ON-PREMISES &
CLOUD)
CONFORM
DATABASES (ON-PREMISES & CLOUD)
COMMIT
Azure SQL DB
OTHER…
Amazon RDS

6. 6© 2019 Attunity 6© 2019 Attunity
SOURCES
CLOUD
Amazon RDS
(SQL Server, Oracle,
MySQL, Postgres)
Amazon Aurora
(MySQL)
Amazon Redshift
Azure SQL Server
M1 (Q1)
ATTUNITY – PLATFORM SUPPORTABILITY
MATRIX
SAP
ECC
ERP
CRM
SRM
GTS
MDG
S/4HANA
(on Oracle, SQL,
DB2, HANA)
DATABASE
Oracle
SQL Server
DB2 iSeries
DB2 z/OS
DB2 LUW
MySQL
PostgeSQL
Sybase ASE
Informix
ODBC
EDW
Exadata
Teradata
Netezza
Vertica
Pivotal
MAINFRAME
DB2 z/OS
IMS/DB
VSAM
FLAT FILES
Delimited
(e.g., CSV, TSV)
TARGETS
FLAT FILES
Delimited
(e.g., CSV, TSV)
STREAMING
Kafka
Amazon Kinesis
Azure Event Hubs
MapR Streams
SAP
HANA
EDW
Exadata
Teradata
Netezza
Vertica
Sybase IQ
SAP HANA
Microsoft PDW
GOOGLE
Cloud SQL (MySQL,
Postgres)
Cloud Storage
Dataproc
PubSub (‘19)
Big Query (Q2)
DATA LAKE
Hortonworks
Cloudera
MapR
Amazon EMR
Azure HDInsight
Google Dataproc
DATABASE
Oracle
SQL Server
DB2 LUW
MySQL
PostgreSQL
Sybase ASE
Informix
MemSQL
Compose support
AZURE
DBaaS (SQL DB)
DBaaS (MySQL,
Postgres)
ADLS
BLOB
HDInsight
Event Hub
SQL DW
Snowflake (Q1)
Databricks (Q2)
AWS
RDS (MySQL,
Postgres, MariaDB,
Oracle, SQL Server)
Aurora (MySQL,
Postgres)
S3
EMR
Kinesis
Redshift
Snowflake (Q1)
Databricks (Q2)
SaaS
Salesforce (Q2)

7. 7© 2019 Attunity 7© 2019 Attunity
DBaaS
STORAGE
HADOOP
STREAMING
DWaaS
OTHER DWaaS
SPARK
ATTUNITY – COMPLETE CLOUD PARTNER ALIGNMENT
1. Replicate support for
Google PubSub
planned for 2019.
2. Google BQ supported
today through GCS or
Kafka. Direct load
planned for Q2/19.
RDS (All)
S3
EMR
Kinesis
Redshift
Snowflake
Databricks
Compose support
All
ADLS , BLOB
HDInsight
Event Hubs
Azure SQL DW
Snowflake
Databricks
DB All
GCS
DataProc
Pub Sub
BigQuery
(2)
(1)

8. 8© 2019 Attunity 8
QLIK AND ATTUNITY
RAW TO READY
17
1
Generate
Streams from
Changed Data
No Impact
(Agentless)
Efficiently
Deliver to
Cloud 2
Merge into
Transactional
View
Provision
New Analytic
Data Set
Create or
Move into
DW, DL or
ADBI
3 Registering
into Catalog
REFINEDELIVER SHARE
Data Movement Data Lake/Data Warehouse Automation Data Catalog / Governance

9. MODERNIZE AND AUTOMATE
DATA INTEGRATION