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Accelerate Innovation:
Databricks and Legacy Data
Precisely and Databricks Partnership Overview
Ashwin Ramachandran | Senior Product Manager
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Precisely: Accelerate Innovation with Databricks and Legacy Data
Data is the hardest part of any data project
Disparate and unreliable
data - major contributing
factors to analytics,
machine learning, and AI
project failures
Inconsistent adoption of
various tools and third-
party embedded
integration utilities
Legacy data is not
readily compatible with
cloud platforms and
cloud data warehouses
Simplify
your
process
Consider
your
architecture
Develop
your
strategy
Understand
your gaps
Precisely: Accelerate Innovation with Databricks and Legacy Data
Don’t leave legacy
data behind
• How fast can you integrate
legacy systems?
• How will you understand
mainframe or IBM i data?
• How can you best optimize for
extreme data volumes?
Precisely: Accelerate Innovation with Databricks and Legacy Data5
Precisely: Accelerate Innovation with Databricks and Legacy Data
Future-proof your environment
Quickly and easily add sources or targets
Design once, deploy anywhere approach
Guaranteed data delivery
True real-time delivery
✔
✔
✔
✔
Joint value: Precisely & Databricks
• Experts in liberating data from
legacy data sources
• Build visual streaming data
pipelines
• Modernize ETL processes and
scale with high-performance
engine
• Capture changes to data in
real-time
• 10-100x faster than Open
Source Spark with Delta as the
core engine for PB scale
processing
• Lowest TCO through auto-
scaling and auto-configuration
capabilities
• Unified, collaborative
experience for data engineers
& data scientists on one
platform
Precisely: Accelerate Innovation with Databricks and Legacy Data
Precisely: Accelerate Innovation with Databricks and Legacy Data
Get data from legacy sources into the data lakehouse!
Want your data from legacy, mainframe and IBM i is loaded as-is to the cloud?
• Easily create an exact bit-for-bit copy of
mainframe and IBM i data on the Cloud
• Work with that data in Spark – native Spark
integration
• Map data directly to copybook in Spark
• End-to-end managed approach for
offloading data
• Directly access and understand VSAM,
mainframe fixed and variable files, and
Db2 data
• Take a design once, deploy anywhere
approach to data integration
• Transform data on the fly – no staging
• Import hundreds or Db2 tables to your
data lakehouse with a few mouse clicks
• Distributed ETL on Databricks
• High-performance, self-tuning sorts,
joins, aggregation, merges, and look-
ups optimized to run natively in the
Databricks run-time
• Dynamically optimize loads for extreme
data volumes
• Break down data silos in minutes
Precisely: Accelerate Innovation with Databricks and Legacy Data
Connect and Databricks
Mainframe
,
IBM i
Relational
databases,
EDW, DBMS
Flat files,
XML, JSON
Ingest
&
Stream
Data
Integrate,
Prepare,
Load,
Cleanse,
Transform
Unified Data Analytics Platform
Reporting and BI
Deliver
Data
Data Sources
Hadoop,
HDFS
Connect’s ETL capabilities
and Databricks eliminate data
silos across your business
Accelerate Innovation with Databricks and Legacy Data

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Accelerate Innovation with Databricks and Legacy Data

  • 1. Accelerate Innovation: Databricks and Legacy Data Precisely and Databricks Partnership Overview Ashwin Ramachandran | Senior Product Manager
  • 2. Housekeeping Webinar Audio • Today’s webcast audio is streamed through your computer speakers • If you need technical assistance with the web interface or audio, please reach out to us using the Q&A box Questions Welcome • Submit your questions at any time during the presentation using the Q&A box Recording and slides • This webinar is being recorded. You will receive an email following the webinar with a link to the recording and slides
  • 3. Precisely: Accelerate Innovation with Databricks and Legacy Data Data is the hardest part of any data project Disparate and unreliable data - major contributing factors to analytics, machine learning, and AI project failures Inconsistent adoption of various tools and third- party embedded integration utilities Legacy data is not readily compatible with cloud platforms and cloud data warehouses
  • 5. Don’t leave legacy data behind • How fast can you integrate legacy systems? • How will you understand mainframe or IBM i data? • How can you best optimize for extreme data volumes? Precisely: Accelerate Innovation with Databricks and Legacy Data5
  • 6. Precisely: Accelerate Innovation with Databricks and Legacy Data Future-proof your environment Quickly and easily add sources or targets Design once, deploy anywhere approach Guaranteed data delivery True real-time delivery ✔ ✔ ✔ ✔
  • 7. Joint value: Precisely & Databricks • Experts in liberating data from legacy data sources • Build visual streaming data pipelines • Modernize ETL processes and scale with high-performance engine • Capture changes to data in real-time • 10-100x faster than Open Source Spark with Delta as the core engine for PB scale processing • Lowest TCO through auto- scaling and auto-configuration capabilities • Unified, collaborative experience for data engineers & data scientists on one platform Precisely: Accelerate Innovation with Databricks and Legacy Data
  • 8. Precisely: Accelerate Innovation with Databricks and Legacy Data Get data from legacy sources into the data lakehouse! Want your data from legacy, mainframe and IBM i is loaded as-is to the cloud? • Easily create an exact bit-for-bit copy of mainframe and IBM i data on the Cloud • Work with that data in Spark – native Spark integration • Map data directly to copybook in Spark • End-to-end managed approach for offloading data • Directly access and understand VSAM, mainframe fixed and variable files, and Db2 data • Take a design once, deploy anywhere approach to data integration • Transform data on the fly – no staging • Import hundreds or Db2 tables to your data lakehouse with a few mouse clicks • Distributed ETL on Databricks • High-performance, self-tuning sorts, joins, aggregation, merges, and look- ups optimized to run natively in the Databricks run-time • Dynamically optimize loads for extreme data volumes • Break down data silos in minutes
  • 9. Precisely: Accelerate Innovation with Databricks and Legacy Data Connect and Databricks Mainframe , IBM i Relational databases, EDW, DBMS Flat files, XML, JSON Ingest & Stream Data Integrate, Prepare, Load, Cleanse, Transform Unified Data Analytics Platform Reporting and BI Deliver Data Data Sources Hadoop, HDFS Connect’s ETL capabilities and Databricks eliminate data silos across your business

Editor's Notes

  1. Look at ways to simplify the creation of real-time analytical applications by cleansing, pre-processing and transforming data in motion Consider your current data integration architecture, was it built to support timely data delivery from a variety of sources Strategies in place for data delivery of mainframe and IBM i data to business applications and analytics What are you lacking for a complete data picture – skills, tools, or time?
  2. Consider the speed in which you need you are able to unlock legacy data for use in AI and ML projects Determine how you will directly access and understand mainframe or IBM i data. Do you have legacy data expertise in-house or will you have to seek external resources to access data? How will you optimize loads for extreme data volumes
  3. Eliminate lock-in to cloud vendors and legacy technology Look for solutions that insulate your organization against the underlying complexities of your technology stack Consider that data delivery requirements for AI and ML may break current data integration methods Select solutions that guarantee data delivery and have reliable transfer of information Assess how your overall cloud strategy can support real-time data delivery to next wave platforms
  4. Precisely Value Propositions Liberate legacy data sources for use within Databricks Unified Data Analytics Platform and Delta Lake by building visual data pipelines with Connect’s ETL offering Modernize ETL processes using the elastic scalability of Databricks Unified Data Analytics Platform and Connect for Big Data high-performance engine. Populate Delta Lake with data changes from legacy systems, such as the mainframe, in real-time with Connect’s CDC capabilities