MongoDB is an open-source document- oriented schema-less database system. It does not organize the data using rules of a classical relational data model. Unlike other relational databases where data is stored in columns and rows, MongoDB is built on the architecture of collections and documents. One collection holds different documents and functions. Data is stored in the form of JSON style documents. MongoDB supports dynamic queries on documents using a document based query language like SQL.
This blog post
This blog post is the final part of the Data Warehouse Migration to AR series. The second part of the blog post series Data Warehouse Migration to Amazon Redshift – Part 2 details on how to get started with Amazon Redshift, the business and technical benefits of using AR.
1. Migrating to AR
The migrating strategy that you choose depends on various factors such as:
- The size of the database and its tables
- Network bandwidth between
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
Apart from the better understanding on data, we need to pay more attention towards the basic statistics as it is the key concept of driving the data to develop interactive visualizations and convert tables into pictures.
The rapid rise of visualization tools such as Spotfire, Tableau, Qlikview and Zoomdata, has gained immense use of graphics in the media. These tools currently hold an ability to transform the data into meaningful information with proper standard level principles in the statistical
This blog post is the second part of the Data Warehouse Migration to AR series. The first part of the blog post series Data Warehouse Migration to Amazon Redshift – Part 1 details on how Amazon Redshift can make a significant impact in lowering the cost and operational overheads of a data warehouse.
1. Getting Started with Amazon Redshift (AR)
Since Redshift is delivered and managed in the cloud, it is mandatory to have an Amazon Web
IBM InfoSphere Master Data Management Collaborative Edition provides a highly scalable, enterprise Product Information Management (PIM) solution that creates a golden copy of products and becomes trusted system of record for all product related information.
Performance is critical for any successful MDM solution which involves complex design and architecture. Performance issues become impedance for smooth functioning of an application, thus obstructing the business to get the best out of it. Periodic profiling and optimizing the application based on the findings
Traditional data warehouses require significant time and resource to administer, especially for large datasets. In addition, the financial cost associated with building, maintaining, and growing self-managed, on-premise data warehouses is very high. As your data grows, you need to always exchange-off what data to stack into your data warehouse and what data to archive in storage so you can oversee costs, keep ETL complexity low, and deliver good performance.
This blog post details on how Amazon Redshift can make
Informatica MDM Multi Domain Edition (MDE) supports multiple Business Data Domains with a flexible data model which allows you to adapt to Data model in line with your business requirements; also provides you the flexibility not to adapt to fixed vendor defined data models. The business rules can be reused across unified MDM, data quality and data integration on a single platform. The granular web services are automatically generated and high level composite services are created for rapid integration.
In the current aggressive business condition, utilizing the data resources of an association is vital to building effective worldwide business endeavors of the future. Multiple organizations burn through a huge number of dollars on business knowledge/information warehousing (BI/DW) arrangements, yet these activities have yielded not as much as expected rate of return (ROI) as they have neglected to distinguish and address the basic angles impacting the result of these BI activities.
Business Intelligence (BI) Editions come in all shapes and
MDM Connector stage is a key to open the door of IBM Virtual MDM. Yes, we can manipulate the data in MDM (MDM refers to IBM Virtual MDM in this post) using the MDM Connector stage which was introduced in IBM DataStage v11.3.
We know that loading data into MDM is not an easy task since it involves many tables and the relationship among the tables should be maintained properly, otherwise will end up dealing with junk not with