clover

Data Integration using CloverDX

We have been CloverDX Partners Since 2012.

CloverDX partner since 2012 (version 3.2)

We are an agile, value centered consultancy that has been a CloverDX partner since 2012 (version 3.2). We value stability and long-term relationship with our customers and employees. Our focus on data integration & CloverDX allows us to provide high quality & high value services in Financial, Health Care and Government space. Our clients treat us as their trusted partner and advisor for on budget and on time deliveries.

DATA INTEGRATION SERVICES

Data Management & Governance

We interview data owners and department heads to understand an organization's vision, goals, and objectives for managing and using its data assets. We provide a strategic framework to accelerate data assimilation and distribution, empowering decision makers with timely, high-quality data.

Enterprise Conceptual Data Model (ECDM)

We work with technical teams to identify the common data elements across different systems and applications within an organization. ECDM serves as a foundation for data integration efforts by providing a common language for data sharing, mapping, and transformation.

Enterprise Conceptual Data Flow

We interact with data teams, application subject matter experts and business analysts across the organization to map and document the flow of data across the various applications within an organization. This process involves creating a high-level conceptual diagram that depicts the movement of data across the enterprise.

Enterprise Data Dictionary & Data Catalog

We work with data stewards, data owners and consumers of data to understand and document definition of data assets across the organization. This helps us to provide a clear and consistent (and sometimes conflicting) perspective of data, which informs Data Integration.

Save Licensing Costs

We can transition from expensive data integration tools like Ab-Initio to more cost-effective MPP (Massively Parallel) options like CloverDX or Spark .

Production Support

ETL Tool Upgrades & Patching

ETL tool upgrading & patching across GDPR and HIPPA compliant development, test and production environments. 

Ad-hoc Execution

We provide support for on-demand execution of pre-designed ETL jobs for new client configuration / data conversion etc.

Production Failure Support

All our ETL jobs are designed to restart gracefully in event of failure. However, if there is failure due to source structure changes,  file layout changes, unexpected special characters, and cache / buffer / file swap space limitations. We Investigate, fix and restart the jobs to ensure the data gets to the right people at the right time.

Reproduce the Failures in Lower Environment

Our Data Integration teams investigate the issues and errors reported by our clients and then replicates these scenarios in lower environments. This typically involves understanding how the production data interacts with data transformation rules encoded in the data integration jobs.

Investigate Load Discrepancies

If our automated data quality agents or our consumers detect issues, our analysts investigate by analyzing the source and target data, mapping, and transformation rules. 

Source Application Release Testing & Support

Export Verification

Verify exports from the applications don’t change in unexpected manner, for both old and new versions of the application.

Import Verification

Test imports into the applications. Verify that the data has been properly imported .

Performance Testing

Conduct Performance Testing of ETL jobs after new application deployment.

Support Client Testing - Test Data Loads

Test Source Data Quality

Profile source data to ensure quality of data provided is good enough for loads.

Customer Master Changes

Ensure the accuracy and completeness of the data being tested. 

Data Source Changes

We work with our clients to support simple changes in source layouts, source structure changes. Refers to the process of validating, verifying, and qualifying data while preventing duplicate records and data loss.

ETL Design and Development

Create & Review Mapping Documents

Our team of data analysts develops a deep understanding of your source systems and target data model, allowing us to meticulously map each target attribute to its corresponding source attribute. We also document the transformation rules in great detail, ensuring development of accurate and precise data integration jobs. 

New ETL development

Our experts specialize in developing ETL pipelines based on mapping documents, data models and data flows. There are times when data integration pipelines are completely redesigned when there are significant changes to existing data sources, new data sources are required, or target requirements have been modified significantly.

Tool Independent Designs

We specialize in creating tool-independent designs that can be easily codified in any data integration tool. We achieve this by combining mapping documents and detailed data flows.

Tool Replacement

Our team can reverse engineer & re-design your current data integration processes to create tool-independent designs, enabling us to seamlessly transition your data integration tool to more modern options.

ETL Execution Operational Report

Report Generation

We can generate a wide range of reports in various formats such as CSV, Formatted Excel, PDF, XML, and JSON. We have flexibility to choose the format that best suits their needs.

Report Delivery

Our team provides a flexible and efficient way to receive reports through scheduled or event-based delivery.

Cross Training

We constantly cross-train our teams. This leads to increased creativity, enhanced collaboration, fast tracks career growth of our employees and reduces single points of failures.

 

Enhancement of Existing Data Integration Jobs

Mapping Review

Our data mappers review the existing data integration mapping documents to ensure that all the necessary rules are included, and that the specifications can be properly implemented based on the available data. 

Test Data Generation

Our team can generate a variety of test data while maintaining data integrity across hundreds of files and millions of records. Our test is also capable of simulating a variety of data integration scenarios, as well as identifying potential failure points to ensure optimal performance.

Source Data Validation

As part of the data profiling process, we not only assess the quality and consistency of the data from the source systems, but also develop a comprehensive data model of the source systems. This involves examining the data both within a system and across different source systems to ensure consistency and accuracy.

Testing and Code-Reviews

Our ETL testing teams play a crucial role in ensuring the efficient functioning of ETL workflows and processes. They meticulously test and review any modifications or updates made to the ETL system to ensure they are error-free before deployment to the production environment. 

Production Deployment

Our team specializes in supporting production deployment pipelines utilizing GIT, which includes working across various GIT repositories and utilizing tools such as GITHub or GITLab.

YPoint’s CloverDX practice provides

24×7 Development
& ETL Support

Breakfix
practice provides

ETL Incidents &
Services Requests

Data Architecture Assessments

Change Data Capture Processes

Change Data Capture Processes

Sample Data Integration Design

Complex Source to Target Mapping

Sample Data Integration Design

Sample Solution Architecture

Delta Detection Design

Comparative Analysis of Data Integration Tools

AttributeCloverDXApache FlinkApache NiFiApache SparkAWS GlueIBM Data stageInformaticaMicrosoft Azure Data FactoryTalend
Main purposeData IntegrationStream processing
framework
Data Integration tool and supports directed
graphs
Unified analytics engine for distributed
processing
Data Integration within AWSData IntegrationSuite of products with DI focusData Integration within AWSData Integration
ProsEasy to use, specific to purpose, support for complex
data types, GraphDB and multimodel DB
Supports batch, real time and graph.
Has a library for CEP
GUI to build dataflows; lineage;Easy to use, community and ecosystemNative integration in AWS ecosystemInflight data qualityLow code data engineering
integration support
Native integration in Azure ecosystemChange Data Capture; Self service data
preparation;
ConsProprietary language, no Change Data Capture or
streaming support
Relatively immature; lack of
enough documentation; no GUI
Not suitable for very complex transformations;
Limited documentation
No Drag-n_Drop GUI to build pipelines.not beginner-friendly; Has limitations
beyond AWS ecosystem
Focus on end to end against
best fit
Real time integration and Data lineage are offered by
different products outside ADF
After sales support challenges
DeploymentOn-premise, cloud, hybridOn-premise, Amazon cloudOn-premise, Amazon cloudOn-premise, Cloud, hybridAWS CloudSaaS, Multicloud, on-premisesOn premises, multicloud,
hybrid
Azure cloud; SSIS for on-premisesOn premises, cloud, hybrid
Code generationGenerated code can be graphically viewed due to
metadata nature of the call.
Limited to ProtoBufNoGUI actions to code generation not available.
Happens internally to improve performance.
YesNot possible.Not possible.YesYes
ScriptingUses business friendly scripting language specifically
designed for data integration.
Scripting in Python, R, Java, Scala, SQLProprietary expression language;Scripting in Python, R, Java, Scala, SQLUI Based no code & Python scriptingBASIC, C, JAVA..Proprietary and JavaUI based no code, Proprietary, Azure Functions in C#,
Javascript, Java, Powershell, Python, Go, Rust..
Proprietary and Java. Also supports Perl,
Python, Javascript
Data Quality supportYesNo direct "profile data" operationNoMany high level APIs but no direct "profile
data" operation
YesYesYesNo direct supportYes
Metadata supportNo / LimitedExternal
e.g. Hive Catalog
NoYesYesYesYesYesYes
Data Governance supportYes
Coarse grained permissions
NoNoExternal
e.g. Apache Atlas
YesYesYesYesYes
Gartner Magic Quadrant 2022Niche PlayerNA because main purpose is differentNANA because main purpose is differentNiche PlayerNA (Part of IBM Cloud pack,
which is a Leader)
LeaderLeaderNA (Data Fabric is leader)
AttributeCloverDXApache FlinkApache NiFiApache SparkAWS GlueIBM Data stageInformaticaMicrosoft Azure Data FactoryTalend
Main
purpose
Data IntegrationStream processing
framework
Data Integration
tool and supports
directed graphs
Unified analytics
engine for
distributed
processing
Data Integration
within AWS
Data IntegrationSuite of products
with DI focus
Data Integration
within
AWS
Data Integration
ProsEasy to use,
specific to purpose,
support for complex
data types, GraphDB
and multimodel DB
Supports batch, real
time and graph.
Has a library for CEP
GUI to build
dataflows;
lineage;
Easy to use,
community and
ecosystem
Native
integration in
AWS ecosystem
Inflight
data quality
Low code data
engineering
integration
support
Native
integration
in Azure
ecosystem
Change Data
Capture; Self
service data
preparation;
ConsProprietary language,
no Change Data
Capture or
streaming support
Relatively immature;
lack of enough
documentation; no GUI
Not suitable for
very complex
transformations;
Limited documentation
No Drag-n_Drop
GUI to build
pipelines.
not
beginner-friendly;
Has limitations
beyond AWS ecosystem
Focus on end
to end against
best fit
Real time integration
and Data lineage
are offered by different
products outside ADF
After sales
support
challenges
DeploymentOn-premise,
cloud, hybrid
On-premise,
Amazon cloud
On-premise,
Amazon cloud
On-premise,
Cloud, hybrid
AWS CloudSaaS, Multicloud,
on-premises
On premises,
multicloud,
hybrid
Azure cloud;
SSIS for on-premises
On premises,
cloud, hybrid
Code
generation
Generated code
can be graphically
viewed due to
metadata nature
of the call.
Limited to
ProtoBuf
NoGUI actions to
code generation
not available.
Happens internally
to improve
performance.
YesNot possible.Not possible.YesYes
ScriptingUses business
friendly scripting
language specifically
designed for
data integration.
Scripting in
Python, R, Java,
Scala, SQL
Proprietary
expression
language;
Scripting in Python,
R, Java, Scala, SQL
UI Based no
code &
Python scripting
BASIC, C, JAVA..Proprietary
and
Java
UI based no code,
Proprietary, Azure
Functions in C#,
Javascript, Java,
Powershell, Python,
Go, Rust..
Proprietary and
Java. Also
supports Perl,
Python, Javascript
Data Quality
support
YesNo direct
"profile data"
operation
NoMany high level
APIs but no direct
"profile data"
operation
YesYesYesNo direct supportYes
Metadata
support
No / LimitedExternal
e.g. Hive Catalog
NoYesYesYesYesYesYes
Data Governance
support
Yes. Coarse
grained permissions
NoNoExternal
e.g. Apache Atlas
YesYesYesYesYes
Gartner Magic
Quadrant 2022
Niche PlayerNA because
main purpose is
different
NANA because
main purpose is
different
Niche PlayerNA (Part of IBM
Cloud pack,
which is a Leader)
LeaderLeaderNA (Data Fabric
is leader)

CloverDX Product Suite we work with

CloverDX Product Suite we work with

YPoint Conversation with CloverDX

Y Point clients using CloverDX / CloverETL

OUR CORE SERVICE OFFERINGS INCLUDE

CloverDX Services

Strategy & Planning

DevOpps Setup

Data Migration/Conversion

Upgrades & Maintenance

Big Data Cluster Designs

Data warehouse Design

Host CloverDX Hassle-Free
We will be responsible for the initial hardware provisioning, installation, setup, configuration, securing, testing, load balancing and tuning of the hosting environment.
Leverage Our CloverDX Expertise
Regular Patch Management, ensuring CloverDX is always updated, code is migrated to the new version and you are taking advantage of the latest features of CloverDX
Get the Most Out of Your Application

Application performance monitoring,production failure monitoring, data analysis and data quality improvement, workflow design and development, enhancements, new developments etc

We have delivered CloverDX with following technologies

We support embedded CloverDX

oracle endeca

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