How to integrate Informatica Data Quality (IDQ) with Informatica MDM?

Overview
Data cleansing and standardization is an important aspect of any Master Data Management (MDM) project. Informatica MDM Multi-Domain Edition (MDE) provides reasonable number of cleanse functions out-of-the-box. However, there are requirements when the OOTB cleanse functions are not enough and there is a need for comprehensive functions to achieve data cleansing and standardization, for e.g. address validation, sequence generation. Informatica Data Quality (IDQ) provides an extensive array of cleansing and standardization options. IDQ can easily be used along with Informatica MDM.
This is image title
This blog post describes the various options to integrate Informatica MDM and IDQ, explains the advantages and disadvantages of each approach to aid in deciding the optimal approach based on the requirements.
To Get in Depth knowledge on informatica you can enroll for a live demo on informatica online training

Informatica MDM-IDQ Integration Options
There are three options through which IDQ can be integrated with Informatica MDM.

  • Informatonline informatica courseQ Cleanse Library
  • Informatica MDM as target

Option 1: Informatica Platform Staging
Starting with Informatica MDM’s Multi-Domain Edition (MDE) version 10.x, Informatica has introduced a new feature called “Informatica Platform Staging” within MDM to integrate with IDQ (Developer Tool). This feature enables to directly stage/cleanse data using IDQ mappings to MDM’s Stage tables bypassing Landing tables.

Advantages
Stage tables are immediately available to use in the Developer tool after synchronization eliminating the need to manually create physical data objects.
Changes to the synchronized structures are reflected into the Developer tool automatically.
Enables loading data into Informatica MDM’s staging tables bypassing the landing tables.
I hope you reach a conclusion about Data Warehousing in Informatica. You can learn more about Informatica from online Informatica online course

#informatica online training #informatica training #online infromatica training #learn informatica online #online informatica course #informatica developer training

What is GEEK

Buddha Community

How to integrate Informatica Data Quality (IDQ) with Informatica MDM?
Virgil  Hagenes

Virgil Hagenes

1602702000

Data Quality Testing Skills Needed For Data Integration Projects

The impulse to cut project costs is often strong, especially in the final delivery phase of data integration and data migration projects. At this late phase of the project, a common mistake is to delegate testing responsibilities to resources with limited business and data testing skills.

Data integrations are at the core of data warehousing, data migration, data synchronization, and data consolidation projects.

In the past, most data integration projects involved data stored in databases. Today, it’s essential for organizations to also integrate their database or structured data with data from documents, e-mails, log files, websites, social media, audio, and video files.

Using data warehousing as an example, Figure 1 illustrates the primary checkpoints (testing points) in an end-to-end data quality testing process. Shown are points at which data (as it’s extracted, transformed, aggregated, consolidated, etc.) should be verified – that is, extracting source data, transforming source data for loads into target databases, aggregating data for loads into data marts, and more.

Only after data owners and all other stakeholders confirm that data integration was successful can the whole process be considered complete and ready for production.

#big data #data integration #data governance #data validation #data accuracy #data warehouse testing #etl testing #data integrations

Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Uriah  Dietrich

Uriah Dietrich

1618457700

What Is ETLT? Merging the Best of ETL and ELT Into a Single ETLT Data Integration Strategy

Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:

  • ETL is valuable when it comes to data quality, data security, and data compliance. It can also save money on data warehousing costs. However, ETL is slow when ingesting unstructured data, and it can lack flexibility.
  • ELT is fast when ingesting large amounts of raw, unstructured data. It also brings flexibility to your data integration and data analytics strategies. However, ELT sacrifices data quality, security, and compliance in many cases.

Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.

#data science #data #data security #data integration #etl #data warehouse #data breach #elt #bid data

How to integrate Informatica Data Quality (IDQ) with Informatica MDM?

Overview
Data cleansing and standardization is an important aspect of any Master Data Management (MDM) project. Informatica MDM Multi-Domain Edition (MDE) provides reasonable number of cleanse functions out-of-the-box. However, there are requirements when the OOTB cleanse functions are not enough and there is a need for comprehensive functions to achieve data cleansing and standardization, for e.g. address validation, sequence generation. Informatica Data Quality (IDQ) provides an extensive array of cleansing and standardization options. IDQ can easily be used along with Informatica MDM.
This is image title
This blog post describes the various options to integrate Informatica MDM and IDQ, explains the advantages and disadvantages of each approach to aid in deciding the optimal approach based on the requirements.
To Get in Depth knowledge on informatica you can enroll for a live demo on informatica online training

Informatica MDM-IDQ Integration Options
There are three options through which IDQ can be integrated with Informatica MDM.

  • Informatonline informatica courseQ Cleanse Library
  • Informatica MDM as target

Option 1: Informatica Platform Staging
Starting with Informatica MDM’s Multi-Domain Edition (MDE) version 10.x, Informatica has introduced a new feature called “Informatica Platform Staging” within MDM to integrate with IDQ (Developer Tool). This feature enables to directly stage/cleanse data using IDQ mappings to MDM’s Stage tables bypassing Landing tables.

Advantages
Stage tables are immediately available to use in the Developer tool after synchronization eliminating the need to manually create physical data objects.
Changes to the synchronized structures are reflected into the Developer tool automatically.
Enables loading data into Informatica MDM’s staging tables bypassing the landing tables.
I hope you reach a conclusion about Data Warehousing in Informatica. You can learn more about Informatica from online Informatica online course

#informatica online training #informatica training #online infromatica training #learn informatica online #online informatica course #informatica developer training

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management