Amazon offers loading data to Redshift either from flat files that are stored in an Amazon S3 bucket or from Amazon DynamoDB table. In this article, I will show you how to load data from your local machine to Amazon Redshift using the Amazon S3 service. Also, for inserting data to Amazon Redshift, I will show you how to use COPY command.
MicroStrategy prefers de-normalized snowflake schemas, but this does not mean it is always necessary to “snowflake” your dimensions. MicroStrategy architecture can work with a star schema as well. This article coves various design considerations for MicroStrategy that includes techniques for certain situations to make MicroStrategy perform as well with a star schema as it does with snowflake schema.
Every snowflake/star schema requirement specific to MicroStrategy is outlined and explained below. In addition, most of these techniques are the preferred way for any Data Mart, regardless of the reporting tool you use.
The success of any company is becoming more and more dependent on unlocking the value of data and turning it into trusted information for critical decision making. The ability to deliver the right information at the right time and in the right context is crucial. Today, organizations are bursting with data, yet most executives would agree they need to improve how they leverage information to prevent multiple versions of the truth, improve trust and control and respond quickly to change.
If you are an IBM customer, it is very likely you have received some level of education about IBM’s Information Management solutions platform, which includes IBM’s Big Data strategy.
In the early 1990’s I was working for a small software company in Seattle that developed mainframe database performance monitors. One day it was announced that we were being acquired by a much larger company. It was the first time in my career I had faced an acquisition and the horror stories I heard from co-workers were unsettling to say the least.
To be honest, the entire experience turned out to be relatively uneventful and even positive. I was able to work with great people who taught me a lot. I clearly remember one afternoon when my new boss chatted with me informally at a corporate retreat and simply asked, “Do you consider yourself a manager or a leader?”
Think about that for just a minute and ask yourself how you would answer that question.
Developing ETL code and attempting to achieve the ultimate business solution can have its challenges. There is always “a need for speed” or real-time business analysis. Using database analytical functions offered by leading database providers (e.g. Oracle, DB2, Microsoft SQL Server 2005 and Teradata) a developer can cut down on the amount of processing within the ETL tool and allow the database to assist with the processing.
IBM released Hadoop-based InfoSphere BigInsights in May 2013. There are already Hadoop-based commercial distributions from other vendors such as Cloudera, HortonWorks and MapR. So it was interesting to learn how IBM stacks up against other vendors in the Big Data landscape. I learned more about this because I had the opportunity to get hands-on with the InfoSphere BigInsights Big Data ecosystem the week of October 7, at an IBM boot camp.
Recently, I had an interesting conversation with my project’s data architect regarding possible back-dated changes for a primary dimension — Employee — in the data warehouse. In our existing data model, Employee was maintained as a Type 2 slowly changing dimension.
Six months into deployment, it was confirmed by business management that employee changes could be back-dated. This conversation reminded me of a project that I was part of three years earlier where such back-dated scenarios happened frequently.
Every successful technology goes through several cycles of invention, discovery, socialization, adoption and continuous improvement. Hadoop is no exception. It has been embraced by early adopters and is now in the “discovery path” for other customers and vendors. The adoption is well supported by third party vendors who have customized and extended their product offerings with their own Hadoop distributions and implementation to help customers adopt the new technology.