At this point in time, the questions I am asking myself are:
- How much of my Data Warehouse environment and processes will eventually be replaced by Hadoop related technologies and processes?
- What ETL processes are best done in Hadoop and which in SQL/SSIS?
- How much of my storage will transfer to Hadoop, Archive, Raw Staged, Operational Stores and Modeled Data?
- How big of Hadoop environment do I need to surpass the power of my current SQL environment?
- Does Hadoop mean adapting new technology to the existing BI strategy or do we need a new BI strategy?
I am tenacious, so it not a matter of “if” but “when” I’ll know which of my old tools will work, how to use new tools and new strategies to conquer the next generation of data challenges.
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The statement highlights the confusion experienced by traditional data warehouse professionals when moving to Apache Hadoop. With years of experience using Microsoft SQL Server for ETL, dimensional modeling, and managing multi-terabyte warehouses, the user is comfortable with structured relational systems where data is processed using SQL-based tools. In such environments, ETL pipelines transform and load business data—such as orders, sales, and web analytics—into well-organized schemas, enabling fast reporting and decision-making for executives.
ReplyDeleteHowever, Hadoop introduces a completely different approach to handling data. Instead of relying on centralized relational databases, Hadoop uses distributed storage (Hadoop Distributed File System / HDFS) and parallel processing frameworks such as Apache MapReduce and Apache Spark. This means familiar concepts like normalized tables, indexes, and traditional ETL workflows are replaced by cluster-based processing of massive structured and unstructured datasets.Big Data Projects. The challenge lies in adapting to new tools, programming models, and ways of thinking, but Hadoop provides major advantages in scalability, fault tolerance, and big data analytics that traditional systems may struggle to handle efficiently.
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