Technological advances, mergers and acquisitions, consolidation, and regulatory compliances have led to significant increases in data sources and volume and a more complex and dynamic business environement than ever before. To successfully implement and manage an enterprise data warehouse, organizations need to develop a strategy to rapidly adapt to these changes. Profiling and creating stable data architecture to consolidate data sources is a growing challenge for businesses. At DataFactZ, we have a proven methodology of implementing large scale data warehouses that enable efficiency: Data Architecture: Establishing an appropriate data architecture that supports the decision-making process is vital to an effective data warehouse. We understand how to design conceptual, logical and physical data models for traditional OLTP systems and dimensional modeling for data warehousing projects. We review and reengineer existing data models to ensure they conform to business rules and naming conventions, ensuring the end results will provide an optimized, scalable model. Our data warehousing experts ensure data
warehouses are built on good foundations that are flexible to serve current and future needs. Data Analysis: Data Analysis ensures the accuracy of data, determines the flow of data through the warehouse and how it integrates with the other data sources. Our proven methodology for data cleansing and analysis includes identifying the right data sources for integration. We ensure that the data profiling and quality of the data in the warehouse is accurate, consistent and standardized. ETL Development: The stability, efficiency and timeliness of the ETL process to load a data warehouse is directly proportional to the success of a data warehousing implementation. We have the knowledge and experience required to handle a robust ETL design and development that will continually add value to the warehouse using the industry-leading ETL tools such Informatica, IBM DataStage, Pentaho, Talend, ODI, SSIS and more. We provide the necessary knowledge to help clients leverage the data in the best way through a variety of data warehousing implementations. We will closely work with business and IT teams throughout each project to provide assistance in
designing data warehouse architecture, implementing ETL modules and providing data analysis services. Our data warehouse implementation services include:
The evolution of big data has created numerous opportunities for organizations to build data warehouses that take full advantage of advanced analytics applications with low cost of ownership. We have implemented several data warehouse solutions on Hadoop using Hive and Cloudera’s Impala technology, which is an open source analytic database for Hadoop capable of mitigating the latency issues of Hive. Impala completely bypasses MapReduce to query the HDFS directly with support for traditional SQL like syntax and built-in data level security. It is ideal for data warehousing-related applications. The diagram below illustrates a high level data warehousing architecture of our typical implementation:
Data Warehouse High Level Architecture
What is data warehousing & modernization? And how does it impact the modern businesses? Modernization is about extending an existing data warehouse infrastructure and leveraging big data technologies to ‘augment’ its capabilities. We have deployed several advanced analytics and big data projects, and have a complete understanding of the benefits of data warehouse modernization. Traditional architectures are not designed to handle the volume, variety and velocity of today’s data-centric business, and require continuous hardware and service investments to gain even minimal performance benefits. These architectures result in over-burdened, costly data warehouses that require anywhere from 3-6 months to add new data sources. With the advent of big data, organizations can benefit from new technologies. Here are the top reasons modernizing your data warehouse is extremely important:
Advanced analytics: The age of analytics is here, and many organizations have invested heavily in reporting and building OLAP applications. However, they are now making a rapid shift towards advanced forms of analytics such as predictive/prescriptive models and leverage the power of big data. Speed: Traditional data warehouses are built on OLTP platforms. These RDBMSs are designed for OLTP data entries that operate on a single record at a time. However, data warehousing operation requires access to a massive number of records in order to perform even simple analytics. In order to improvise the performance on these OLTP databases and support the data warehouse for running massive queries, organizations and RDBMS vendors have resorted to design tricks such as materialized views, aggregate tables, data partitioning strategies and indices. Data warehouse implementations often face problems with limited storage, processing power and human resources required to maintain this approach. This approach is also incapable of providing an environment for real time analytics.
Organizations have started to realize the importance of “bringing time critical situational awareness to data” which can only be achieved with real-time analytics, bringing analytics closer to real-time business operations. The answer is obvious and comes down to SPEED. Scale: Typical data warehouses tend to grow in size quickly, causing costly issues with scalability and performance. With, big data, organizations can take full advantage of commodity hardware create a flexible, data centric solution. Productivity: Traditional SDLC methods of requirements gathering, prototyping and development takes months. Organizations have adopted agile development methods where frequent deliverables are accomplished in data warehousing, business intelligence and analytics. Data warehouse modernization provides the ability to leverage data such as social, mobile and e-mail to identify new metrics that may be better to predict behavior. It also increases an organization’s ability to massage and parse unstructured data
(e.g., log files, text files), to uncover predictive measures in the unstructured data, and quickly feed that data into the existing data warehouse. These new metrics are easily integrated into an organization’s existing business intelligence queries, reports, dashboards and analysis, all of which increase productivity and lead to faster data discovery, profiling and data visualization production. Costs: Data warehouse modernization not only increases an organization’s ability to increase speeds and feeds in the data warehouse environment, but it also provides a great opportunity to optimize the overall costs in areas such as storage and upgrades. Modernization does not necessarily mean a complete overhaul of a data warehouse. This approach identifies and eliminates existing investments that are not producing ROI. We understand the end to end requirements to modernize your data warehouse with a balanced approach. We augment data warehouses to create scalable solutions capable of accommodating big data initiatives and real time analytics.