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SnapLogic Live: Big Data Integration
Welcome to SnapLogic Live
In 30 minutes we’ll:
• Review specific topics and the role SnapLogic plays
• Dive into demonstrations of our Elastic Integration Platform
• Open it up for questions and discussion
Today’s featured presenter: Rich Dill
Today’s featured topic: Big Data Integration
Anything
apps | APIs | things | data
SnapLogic: Unified Platform for Data and Application Integration
Anytime
batch | streaming | real-time
Anywhere
on premises | in the cloud
Common SnapLogic Elastic Integration Use Cases
Hybrid
Application
Integration
Cloud Data
Warehouse/Anal
ytics
Big Data Ingest-
Transform-
Deliver
RESTful
Retire Legacy
Integration
Platforms
z
Data
Acquisition
Data Access
z
Data
Management
Add information
and improve
data
Spark
Python
Scala
Java
R
Pig
Collect and
integrate data
from multiple
sources
HDFS
AWS S3
MS Azure Blob
On Prem Apps
and Data
• ERP
• CRM
• RDBMS
Cloud Apps
and Data
• CRM
• HCM
• Social
IoT Data
• Sensors
• Wearables
• Devices
Lakeshore
Data Mart
• MS Azure
• AWS
Redshift
BI /
Analytics
• Tableau
• MS
PowerBI /
Azure
• AWS
QuickSight
Organize and
prepare data
for
visualization
HDFS
AWS S3
MS Azure Blob
Hive
Batch
Streaming
Schedule and manage:
Oozie, Ambari
Kafka, Sqoop, Flume
Real-time
Impala, HiveSQL,
SparkSQL
Data Lake Components without SnapLogic
z
Data
Acquisition
Data Access
z
Data
Management
The Modern Data Lake
Powered by SnapLogic
On Prem Apps
and Data
• ERP
• CRM
• RDBMS
Cloud Apps
and Data
• CRM
• HCM
• Social
IoT Data
• Sensors
• Wearables
• Devices
Lakeshore
Data Mart
• MS Azure
• AWS
Redshift
BI /
Analytics
• Tableau
• MS
PowerBI /
Azure
• AWS
QuickSight
Batch
Streaming
Schedule and manage:
SnapLogic
Real-time
Sort,
Aggregate,
Join, Merge,
Transform
SnapLogic
abstracts and
operationalizes
with
SnapReduce or
Spark pipelines
Collect and
integrate data
from multiple
sources
SnapLogic
pipelines with
standard mode
execution
Organize and
prepare data
for
visualization
SnapLogic
pipelines with
standard mode
execution
Pipeline
Pipeline
Pipeline
Pipeline
Pipeline
SnapLogic and the Data Lake
SnapLogic’s Modern Architecture: Hybrid and Elastic
Streams: No data is
stored/cached
Secure: 100%
standards-based
Elastic: Scales out &
handles data and
app integration use
cases
Metadata
Data
Databases
On Prem
Apps
Big Data
Cloud Apps
and DataCloud-Based Designer, Manager,
Dashboard
Cloudplex
Groundplex
Hadooplex
Sparkplex
Firewall
Demonstration:
Snaplogic.com/live
Discussion
snaplogic
Unified Platform Self-Service UX
Modern Architecture Connected: 350+ Snaps

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SnapLogic Live: Big Data Integration

  • 1. SnapLogic Live: Big Data Integration
  • 2. Welcome to SnapLogic Live In 30 minutes we’ll: • Review specific topics and the role SnapLogic plays • Dive into demonstrations of our Elastic Integration Platform • Open it up for questions and discussion Today’s featured presenter: Rich Dill Today’s featured topic: Big Data Integration
  • 3. Anything apps | APIs | things | data SnapLogic: Unified Platform for Data and Application Integration Anytime batch | streaming | real-time Anywhere on premises | in the cloud
  • 4. Common SnapLogic Elastic Integration Use Cases Hybrid Application Integration Cloud Data Warehouse/Anal ytics Big Data Ingest- Transform- Deliver RESTful Retire Legacy Integration Platforms
  • 5. z Data Acquisition Data Access z Data Management Add information and improve data Spark Python Scala Java R Pig Collect and integrate data from multiple sources HDFS AWS S3 MS Azure Blob On Prem Apps and Data • ERP • CRM • RDBMS Cloud Apps and Data • CRM • HCM • Social IoT Data • Sensors • Wearables • Devices Lakeshore Data Mart • MS Azure • AWS Redshift BI / Analytics • Tableau • MS PowerBI / Azure • AWS QuickSight Organize and prepare data for visualization HDFS AWS S3 MS Azure Blob Hive Batch Streaming Schedule and manage: Oozie, Ambari Kafka, Sqoop, Flume Real-time Impala, HiveSQL, SparkSQL Data Lake Components without SnapLogic
  • 6. z Data Acquisition Data Access z Data Management The Modern Data Lake Powered by SnapLogic On Prem Apps and Data • ERP • CRM • RDBMS Cloud Apps and Data • CRM • HCM • Social IoT Data • Sensors • Wearables • Devices Lakeshore Data Mart • MS Azure • AWS Redshift BI / Analytics • Tableau • MS PowerBI / Azure • AWS QuickSight Batch Streaming Schedule and manage: SnapLogic Real-time Sort, Aggregate, Join, Merge, Transform SnapLogic abstracts and operationalizes with SnapReduce or Spark pipelines Collect and integrate data from multiple sources SnapLogic pipelines with standard mode execution Organize and prepare data for visualization SnapLogic pipelines with standard mode execution Pipeline Pipeline Pipeline Pipeline Pipeline SnapLogic and the Data Lake
  • 7. SnapLogic’s Modern Architecture: Hybrid and Elastic Streams: No data is stored/cached Secure: 100% standards-based Elastic: Scales out & handles data and app integration use cases Metadata Data Databases On Prem Apps Big Data Cloud Apps and DataCloud-Based Designer, Manager, Dashboard Cloudplex Groundplex Hadooplex Sparkplex Firewall
  • 9. Discussion snaplogic Unified Platform Self-Service UX Modern Architecture Connected: 350+ Snaps

Notas do Editor

  1. Leading enterprises choose SnapLogic because we help them connect data and applications faster. We connect anything: sources including applications, APIs, things, or data We connect anytime: in batches, streaming, or in real time And we connect anywhere: on premises, in the cloud or a combination of both
  2. Here is an example of a SnapLogic deployment. The SnapLogic control plane – including he Designer, Manager and Dashboard - does not store your data. It’s metadata only. Once a pipeline is executed, it looks for the associated Snaplex or Hadooplex. The plex dynamically scales out, adding more nodes as needed. We like to say that SnapLogic “respects data gravity” and runs as close to the data as need be. If you are integrating only cloud applications, it would make no sense to run your integrations behind the firewall. Similarly, if you’re doing ground to ground or cloud to ground, you may want to run your Snaplex on Window or Linux servers. Note that the dotted line is sending instructions via metadata to the plex, which is waiting to run. The solid line indicates how data movies bi-directionally between systems.
  3. http://video.snaplogic.com/demos/