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.
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.
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.
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.
We can transition from expensive data integration tools like Ab-Initio to more cost-effective MPP (Massively Parallel) options like CloverDX or Spark .
ETL tool upgrading & patching across GDPR and HIPPA compliant development, test and production environments.
We provide support for on-demand execution of pre-designed ETL jobs for new client configuration / data conversion etc.
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.
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.
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.
Verify exports from the applications don’t change in unexpected manner, for both old and new versions of the application.
Test imports into the applications. Verify that the data has been properly imported .
Conduct Performance Testing of ETL jobs after new application deployment.
Profile source data to ensure quality of data provided is good enough for loads.
Ensure the accuracy and completeness of the data being tested.
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.
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.
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.
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.
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.
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.
Our team provides a flexible and efficient way to receive reports through scheduled or event-based delivery.
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.
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.
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.
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.
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.
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.
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.
Complex Source to Target Mapping
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) |
A Failed BI/DW implementation was turned around
Operational Data Store & Business Intelligence Reporting
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.
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!