Accelerating Migration from Appliance to Snowflake's Data Cloud - www.hexaware.com
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Accelerating Migration from
Appliance to Snowflake’s Data Cloud
www.hexaware.com
Foreword 03 Introduction – Evolution of Data Warehouse Systems 04 What is Cloud Datawarehouse? 05 What makes Snowflake Stand out? 06 Challenges in migrating an on premise DW Appliance 06 to Snowflake’s Data Cloud Amaze® – Accelerating migration to Snowflake’s Data Cloud 07 Migration with Amaze® 07 Value Delivered by Amaze® 08 Conclusion 08
Foreword
Data is a major asset for enterprises as they are 247% year on year. Organizations have realized
looking for newer ways to derive additional insights cloud’s infinite capacity and elasticity, deliver the
from their data , move towards a data driven deepest, data-driven insights at lesser cost.
decision making. This would be possible only by Snowflake delivers enterprises zero-management
having stronger foundation of data management cloud-built data warehouse, that easily and securely
principles. Enterprises want to have a scalable data loads, integrates, and analyzes all types of data.
management solution that can handle unpredictable
As enterprises embark on cloud journey, the main
data growth, deliver real time insight and enable
consideration would be, how quickly can I get on to
secured data democratization across the enterprise.
cloud with minimum disruption to my current
Data Cloud platforms like Snowflake has come up
business? Hexaware’s amaze® platform and
with modern cloud native architecture to support
framework brings automation to help enterprises
the changing dynamic business need.
adopt cloud DW and Analytics services rapidly
Snowflake customers have almost tripled since without risk to business at lesser cost.
2018, fueling their revenue growth by whooping
2018
2020 247%
year on year
Snowflake’s Revenue growth
Being Hexaware’s own proprietary
platform with no embedded 3rd party
product, amaze® brings down cloud
transformation cost and accelerates
realization of TCO savings.
03
Introduction
Evolution of Data Warehouse Systems
Increase in e-commerce and customer facing applications in With the next big thing of “Big Data” Hadoop era, access to
early 2000’s, saw an explosion in data, leading to an increased MPP architecture was available for everyone, it also handled
demand for larger Data Warehouse systems. Traditional more complex challenges of unstructured data. Hadoop
RDBMS systems on Symmetric Multi- Processing (SMP) ecosystem had massive management overhead arising due to,
architecture such as Oracle were not able to manage large too many tools in the ecosystem and had integration
volume of data and the querying capability required for a DW. challenges with existing applications. DW Appliances over
Appliances such as Teradata, Netezza, built on Massive Parallel cloud with integrated object stores supporting Hadoop
Processing (MPP) architecture solved this problem and they capabilities paved way for Cloud data warehouse systems such
handled Terabytes (TB) of data with ease. However, cost of as Redshift, SQL DW, but these were on-prem platforms fitted
ownership was high, hence it was not affordable for all into a cloud architecture, they were not cloud native.
organizations.
High
- PaaS
DW
Appliance
ud
Clo
Hadoop
Value Delivered
2010
MS
RDB
1990s 2000
1980s
Low
High Cost Associated Low
04
What is Cloud
Datawarehouse?
DW on Cloud provides all features of traditional DW in addition Cloud DW provides elasticity, where in we could increase or
to be a single interface for handling structured, semi structured decrease the cloud resources based on demand, with out any
or unstructured data in a SAAS or PAAS model. intervention from the infrastructure support team in a
self-service mode. Hence management overhead, like running a
The two key requirements to run a data warehouse are storage
YARN software, is not required when we move to cloud. Elastic
and processing. Hadoop was able to fulfill the need however it
storage is provided on cloud by object store platforms such as
comes with a massive ecosystem management. Moving to cloud
AWS S3, Azure Blobstore, Google Cloud Storage and
minimizes this problem, as cloud services provide infrastructure
processing capability that supports advanced analytics are
management along with interfaces as an API.
provided by Snowflake, Redshift and
Azure Synapse.
Following are the additional advantages of Cloud DW vis-à-vis on-premise traditional data warehouse:
High Scalability
Scale resources on-demand Availability & Security
with shorter or nil downtime
99.9%+ availability SLAs with
infrastructure & data security options
Greater Performance
With resource scalability and
replication to the closest region
Reduced TCO
Substantial savings on set up,
maintenance and licenses costs
While all cloud DW platforms provide elastic scaling of storage DW product, another important criterion will be variety of data
and processing required for analytical purpose, Snowflake has a supported. Most of the products have their own support for
provision to independently scale up storage and compute. semi-structured data, for instance Redshift stores JSON as text
Snowflake being a cloud native product even provides field, Azure Synapse needs polybase functionality. Snowflake
interrupt-free scaling up, that is queries can still be running natively supports both structured and semi-structured data.
when we scale up the processor. While choosing the right cloud
05
What makes Snowflake Stand out?
Snowflakes’ Data Cloud platform provides support for data both structured and semi-structured with the option to
warehousing and analytics and is provided as a SaaS model independently scale up storage and compute, compatible with
(Software as a Service). It is built ground up for cloud, is faster, popular ETL, BI and data science tools with SQL interfaces and
easier to use, measurable utilization linked to the cost saving cost on environmental maintenance while compared to
supporting the basic cloud principles. Snowflake’s platform traditional DW. Given below are its key features and
provides support for data warehousing and is optimized to advantages:
handle massive data, it is optimized to handle massive data –
Separate storage and Higher performance Cost savings in terms of Deployment option Data sharing with users
compute environment without administrations, auto suspend mode available on all three outside the organization
with isolated scaling don’t need to create when the queries are major public clouds – using snowflake reader
up options indexes and partitions not running AWS, Amazon & Google accounts
Challenges in migrating an on premise DW Appliance to Snowflake’s Cloud Data Platform
DW appliance migration to Snowflake can only be successful if Each of the DW appliance such as Teradata, Netezza have
we could bring in all the business requirements “as is” without defined their own specific SQL functions that are unique to
impacting the business. While we do that, we also need to keep their environment, understanding and redesigning them is
in mind to re-platform the system in the best possible way to another challenge.
leverage all the new technological advancements brought forth
DW Appliances would have reached their end of life and maxed
by Snowflake. Addressing this need is the foremost migration
out their capacity to process data, we will need to plan to
challenge and this must be done in the best possible way.
extract the data from the appliances for data migration without
DW appliances running on an enterprise for loads of years and impacting the existing appliance processing capacity.
over a period lot of them end up accumulating data processing
Getting a clear visibility of the impact of the database objects
captured in their script objects like procedures, views or
on the ETL layer and reporting layer, ensuring no impact to
macros. This has led to a huge collection of procedural scripts
these layers especially the reporting layer is a bigger challenge.
and understanding the complexity to determine their scope in
migration is a big challenge.
06Amaze® – Accelerating
migration to Snowflake’s
Cloud Data Platform
Amaze® is an automation platform that specifically addresses the migration issues when moving from appliances such as Teradata,
Netezza, Exadata and accelerates the journey to cloud with minimum risk to business at lesser cost.
Migration with Amaze®
Hexaware has a well-defined Microsoft certified Appliance DW existing environment complexity and critical points. Amaze®
Migration process that is enabled by the automation platform platform has tools that automates metadata analysis of
amaze®. Based on the existing scenario of an enterprise in the database, ETL and reporting layer objects, in order to derive
cloud adoption the appropriate phases from the Hexaware inventory for migration, complexity and dependency analysis.
amaze® 4-step transformation journey is applied. Complete understanding of current inventory to define the
scope of migration is achallenging task as it involves all the
developments that have happened over years. Understanding
of current environment is important for not only deciding on
Amaze® delivers the end state architecture but also in coming up with effort
needed to reach there. This typically takes 2 to 4 weeks.
Rapid insight on the current state of
data landscape Enterprise has decided cloud, architecture, has visibility on the
Migration of data base and database complexity and is looking for automating the transformation:
auxiliary objects to Snowflake MVP: Amaze® based Pilot solution to demonstrate the
landscape covering all layers of Data automation possibility to perform transformation rapidly to
Pipeline, Data Store and Data shrink the timeline and deliver value at minimal cost. MVP
Analytics would also enable the team to test some of the challenging use
cases, approach for migrating non-supported objects, and
finalize timelines for overall migration project This is typically
takes 2 to 4 weeks.
Enterprise is looking for a factory-based execution model to
deliver rapidly:
Transform, full-fledged cloud transformation applying
Hexaware migration framework and the amaze® platform. With
all the planning and design in place, transform phase sets in
factory mode migration with multiple iterations. Based on
Enterprise is exploring cloud: design decisions, amaze’s® configurations are updated across all
Assessment: Try to identify best fit for purpose cloud stack threelayers and the actual migration process happens that
using questionnaire and cloud readiness scoring. The scoring covers data objects including tables, views, procedures and
model has been jointly built with TDWI, this typically takes 1 to other scripts, ETL packages , BI semantic layer and reports. This
2 weeks. will be a sprint based delivery varying from 3 to 12 months
based on environment complexity.
Enterprise has decided cloud, but feels the transformation is risky:
Deep discovery: Apply amaze® to create visibility on the
07Value Delivered by Amaze®
Amaze® is an end to end cloudification platform which brings in automation across all the three layers Data Pipeline/Data/Data Analytics.
Key value proposition of amaze® includes accelerated delivery with in-built smartness to ensure cost effective solution.
Successful migration journey with Amaze®
Leveraging Amaze® to transform on-premise 100 TB EDW for our Mining client based out of North America to Snowflake, helped
us reduce cost and efforts by 60%. Their decade old on-premise EDW was built on Teradata and Hadoop environments. Automation
helped us not only to transform Teradata and Hive data objects, scripts and data to Snowflake to deliver ‘ÁS IS’ functionality, but
also delivered enhanced real time analytics through HVR, Snowpipe for use cases like driver safety, video analytics.
01 02 03
Accelerated Smarter Cost Effective
Automated data landscape 30+ metadata adapter Cost savings up-to 60%
discovery intelligence through Automation
Automated data, pipelines and In-built knowledge of on-prem No embedded licensed products
analytics migration to cloud platform gaps
End to end managed services
Matured migration process Impact alerts across the across all data layers
framework data layers
Additional services on Data
Optimized cloud services Metadata driven Data Ingestion Glossary
recommendation Framework
Conclusion
Modernizing EDW on Cloud platform with Snowflake could be among the cost optimization project that any enterprise could take up.
One of the primary considerations, apart from cloud readiness, will be the initial cost of migration. Using amaze ® the time, effort and
cost to migrate to cloud would be reduced, hence providing the impetus to move ahead.
To assess your cloud readiness absolutely free and to know more about the cloud and database migration capabilities of amaze ®,
please write to us at amaze@hexaware.com.
References
2019 report, “Magic Quadrant for Data Management Solutions for Analytics.”
Snowflake Documentation - https://docs.snowflake.com/en/user-guide/intro-key-concepts.html
https://dzone.com/articles/hadoop-vs-database-vs-cloud-dwh
08Authors
Aruna Narayan Muneeswara Pandian C
Senior Consultant Associate Vice President
About Hexaware
Hexaware is the fastest growing next-generation provider of IT, BPO and consulting services. Our focus lies on taking a leadership position in helping our clients attain customer
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We are now on a journey of metamorphosing the experiences of our customer’s customers by leveraging our industry-leading delivery and execution model, built around the
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