Data Warehousing

SWOT Analysis SWOT is an acronym that stands for Strengths, Weaknesses, Opportunities and Threats. It is an analysis commonly used to take important business decisions. The essential target of a SWOT analysis is to help associations build up a full consciousness of the considerable number of elements required in a choice. This technique was made in the 1960s by Edmund P. Learned, C. Roland Christensen, Kenneth Andrews and William D. Book in their book "Business Policy, Text and Cases" (R.D. Irwin, 1969). Apart from business and industry, SWOT analysis is used in community health and development, education and personal growths. SWOT analysis are commonly used in the following scenarios: Settle on choices about the best way for your initiative. Distinguishing your chances for achievement in setting of dangers to achievement can elucidate bearings and decisions. Modify and refine plans mid-course. Another open door may open more extensive roads, while another risk could...

Extract, Transform and Load ETL stands for Extract, Transform and Load which is a process in database particularly in data warehousing. Data is extracted from homogeneous/heterogeneous data sources in the data extraction phase. The data is transformed into required format in the data transformation phase and the transformed data is loaded into to the new or destined database in the data load phase. Since the data extraction requires significant investment, it is basic to execute the three stages in parallel. While the data is being removed, another change procedure executes while handling the data effectively got and sets it up for stacking while the data stacking starts without sitting tight for the fulfillment of the past stages. ETL frameworks usually incorporate data from various applications (frameworks), commonly created and upheld by various sellers or facilitated on particular PC equipment. The divergent frameworks containing the first data are every now and again oversaw a...

Why? What? And How? Are the three most important questions when it comes migrating a data warehouse. ·   Why should the data warehouse need to be migrated? ·    What should be migrated? Whether only specific components or the entire facility? ·    How can the data warehouse be migrated by mitigating the risks? If the organization that is planning to migrate its data warehouse has the right answer to these questions, then migration of data warehouse can be an easy task. To start with, even before settling on the specifics or the subtle elements of the relocation, it is vital to ensure that the movement is in accordance with the objectives or goals of the company. The top level management of the organization is responsible for setting the short term and long term goals for the data warehouse migration. They should along these lines distinguish the short and long-term goals of the company and how they can be met by the new data warehouse plan.  ...

Common Mistakes for failure of Data Warehouse Project Failure: The most common mistakes for failure of Data Warehouse Project Failure are: Assumption of data warehouse projects as common as other tech projects: The engineers who are talented at building your item and website are ordinarily used to working with totally extraordinary data advances than are required when building data pipelines. Also, huge data advancements like Hadoop, EMR, and Storm have genuine expectations to learn and adapt. Will your engineers learn new aptitudes? Completely! Be that as it may, would you be able to bear to remove time from taking a shot at your center item? Ignoring long-term maintenance: Some common maintenance costs that most organizations forget are: Increase in data velocity. Time and cost of adding new data connections. Time and cost of fixing broken data connection. Data formats vary over time. Requesting for new features such as columns, dimensions and derivatives....

Gartner’s BI Maturity Model Gartner’s Maturity Model has five different levels of maturity: Unaware Tactical Focused Strategic Pervasive This model is most utilized for the assessment of input effort, Business Intelligence (BI) and Performance Management (PM) maturity. The key areas of this assessment are people, metrics & technology and processes. Let us discuss each level of the model briefly: Unaware: This maturity level is regularly portrayed in the writing as "data anarchy", whose markers are conflicting data, erroneous and conflicting data interpretation, and consistent changes attempting to satisfy individual or departmental data needs. Utilization of spreadsheets is high, while utilization of reporting tool is constrained. An organization does not have characterized measurements for execution administration. An organization is not committed to and does not comprehend the significance of the BI and PM. Data administration is left to the IT offi...