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.

Technologies

At Y Point, we prioritize data quality, data integrity, and business rules validation as part of our data integration designs. Regardless of the technology in use, we are committed to ensuring that all our jobs are performance optimized and designed to be re-executable in the event of failures.

We work with most data formats and seamlessly integrate AI & NLP processing into our flows to create a comprehensive solution for data integration needs of our clients. When working on MPP (Massively Parallel Processing) technologies, our designs scale both vertically and horizontally with infrastructure, negating the need to re-design or re-deploy when data volumes grow.

Sample Data Integration Design

Complex Source to Target Mapping

Sample Solution Architecture

Comparative Analysis of Data Integration Tools

Attribute Apache Flink Apache NiFi Apache Spark AWS Glue CloverDX IBM Data stage Informatica Microsoft Azure Data Factory Talend
Main purpose Stream processing
framework		 Data Integration tool and supports directed
graphs		 Unified analytics engine for distributed
processing		 Data Integration within AWS Data Integration Data Integration Suite of products with DI focus Data Integration within AWS Data Integration
Pros 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 Easy to use, specific to purpose, support for complex
data types, GraphDB and multimodel DB		 Inflight data quality Low code data engineering
integration support		 Native integration in Azure ecosystem Change Data Capture; Self service data
preparation;
Cons 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		 Proprietary language, no Change Data Capture or
streaming support		 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
Deployment On-premise, Amazon cloud On-premise, Amazon cloud On-premise, Cloud, hybrid AWS Cloud On-premise, cloud, hybrid SaaS, Multicloud, on-premises On premises, multicloud,
hybrid		 Azure cloud; SSIS for on-premises On premises, cloud, hybrid
Code generation Limited to ProtoBuf No GUI actions to code generation not available.
Happens internally to improve performance.		 Yes Generated code can be graphically viewed due to
metadata nature of the call.		 Not possible. Not possible. Yes Yes
Scripting Scripting in Python, R, Java, Scala, SQL Proprietary expression language; Scripting in Python, R, Java, Scala, SQL UI Based no code & Python scripting Uses business friendly scripting language specifically
designed for data integration.		 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 No direct "profile data" operation No Many high level APIs but no direct "profile
data" operation		 Yes Yes Yes Yes No direct support Yes
Metadata support External
e.g. Hive Catalog		 No Yes Yes No / Limited Yes Yes Yes Yes
Data Governance support No No External
e.g. Apache Atlas		 Yes Yes
Coarse grained permissions		 Yes Yes Yes Yes
Gartner Magic Quadrant 2022 NA because main purpose is different NA NA because main purpose is different Niche Player Niche Player NA (Part of IBM Cloud pack,
which is a Leader)		 Leader Leader NA (Data Fabric is leader)
Attribute Apache Flink Apache NiFi Apache Spark AWS Glue CloverDX IBM Data stage Informatica Microsoft Azure Data Factory Talend
Main
purpose		 Stream processing
framework		 Data Integration
tool and supports
directed graphs		 Unified analytics
engine for
distributed
processing		 Data Integration
within AWS		 Data Integration Data Integration Suite of products
with DI focus		 Data Integration
within
AWS		 Data Integration
Pros 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		 Easy to use,
specific to purpose,
support for complex
data types, GraphDB
and multimodel DB		 Inflight
data quality		 Low code data
engineering
integration
support		 Native
integration
in Azure
ecosystem		 Change Data
Capture; Self
service data
preparation;
Cons 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		 Proprietary language,
no Change Data
Capture or
streaming support		 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
Deployment On-premise,
Amazon cloud		 On-premise,
Amazon cloud 		On-premise,
Cloud, hybrid		 AWS Cloud On-premise,
cloud, hybrid		 SaaS, Multicloud,
on-premises		 On premises,
multicloud,
hybrid		 Azure cloud;
SSIS for on-premises		 On premises,
cloud, hybrid
Code
generation		 Limited to
ProtoBuf		 No GUI actions to
code generation
not available.
Happens internally
to improve
performance.		 Yes Generated code
can be graphically
viewed due to
metadata nature
of the call.		 Not possible. Not possible. Yes Yes
Scripting Scripting in
Python, R, Java,
Scala, SQL		 Proprietary
expression
language; 		Scripting in Python,
R, Java, Scala, SQL		 UI Based no
code &
Python scripting		 Uses business
friendly scripting
language specifically
designed for
data integration.		 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		 No direct
"profile data"
operation		 No Many high level
APIs but no direct
"profile data"
operation		 Yes Yes Yes Yes No direct support Yes
Metadata
support		 External
e.g. Hive Catalog		 No Yes Yes No / Limited Yes Yes Yes Yes
Data Governance
support		 No No External
e.g. Apache Atlas		 Yes Yes. Coarse
grained permissions		 Yes Yes Yes Yes
Gartner Magic
Quadrant 2022		 NA because
main purpose is
different		 NA NA because
main purpose is
different		 Niche Player Niche Player NA (Part of IBM
Cloud pack,
which is a Leader)		 Leader Leader NA (Data Fabric
is leader)

Delta Detection Design

Clients

U.S. OFFICE OF PERSONNEL MANAGEMENT
medispend
Securities & Exchange Commission – SEC
Alcohol Tobacco & Firearms (ATF)
DCHA
ADT
Health Quality Innovators
Virgin Islands Housing Authority
Naamly
CloverDX
uSell

Case Studies

Health Insurance Provider

A Failed BI/DW implementation was turned around 

Pain Points​

  • EDW not able to process claims adjustments accurately and timely.​
  • Convoluted data integration, resulting in troubleshooting impossible.​
  • ETL overhead of first loading normalized model and then load the dimensional DW.​
  • Changing Data sources severely impacted the data quality.

Solution

  • Physical normalized data model dropped.​
  • Input taken from business and physically normalized data layer to create Enterprise Conceptual Data Model (ECDM).​
  • Data quality rules from ECDM applied in the ETL.​
  • ETL design created and vetted with business. ​
  • ETL fixed and re-designed  for performance​

Benefits

  • Greater alignment with business.​
  • Significantly lower integration complexity.​
  • Maintenance costs reduced by 75%.​
  • New data sources were easily added.​
  • Reports and extracts were generated and distributed within the required timeframe.​
  • Claims adjustments were accurately processed.​

SeniorLink Caregiver Homes

Operational Data Store & Business Intelligence Reporting

Pain Points

  • Lack of enforcement of business rules in Senior touch application resulting in incorrect event sequence / dates.​
  • Lack of clear definitions of fundamental business events. (Placement, Suspension, Reactivation, Closed etc)​
  • No customer master, no organization hierarchy master.​
  • Non-Standard reference data across states.​
  • 4-5 different teams working in a disorganized manner.​
  • Regular updates to historical transactions.​
  • Loose definition of reporting metrics.​
  • Legacy manual excel & stored proc based reporting solution with fluctuating counts and manual interventions.​

Direct Benefits

  • Standardized Rolling Census Reporting across all states.​
  • Accurate counts for Rolling Census and Population Metrics like:​
  • Report creation time down from 4 hours to 15 minutes. ​
  • Reports updated daily and available at 12:15 AM instead of 12 PM.

Indirect Benefits

  • Kick started the Data Governance process.
    • Creation Of Seniorlink Corporate Hierachy.
    • Clean Definitions of key business events.
    • Identification of data quality issues.
  • Increase the shelf life of senior touch application.
  • Ability to easily integrate MDS, Financial data to create one version of truth.

Testimonials

medispend

Medispend 

Director of Data Opps 

As a director of Data Opps, I managed Y Point Analytics team from 2019-2022. In my 25+ years of managing teams, I have yet to come across a team with such high ethics, team work & passion to deliver. YPoint team functions like a well oiled machine, taking in complex data integration and analytic requirements and creating high-quality, well documented, performance optimized code that continues to run for years. They do all this while juggling 100’s of data pipeline requests on a regular basis. This results in minimum production issues, satisfied clients, and happy employees.

GSK

GSK

Director

YPoint Solutions did a fantastic job implementing a state of the art 'Digital Assistant' for US Medical Affairs. The entire team was great to work with, and tailored solutions specific to our needs at GlaxoSmithKline. Amazed at how quickly the solution was implemented from the time initial conversations started. Highly recommend!

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