What is Modern Data Warehouse?
Modern Data warehouse comprised of multiple programs impervious to User. Polyglot persistence encourages the most suitable data storage technology based on data. This “best-fit engineering” aligns multi-structure data into data lakes and considers NoSQL solutions for JSON formats. Pursuing a polyglot persistence dat strategy benefits from virtualization and takes advantage of the different infrastructure. Modern DW requires Petabytes of storage and more optimized techniques to run complex analytic queries. The traditional methods are relatively less efficient and not cost-effective to fit into the modern day Data Warehousing needs. There are tons of Cloud solutions to build data warehouses performance optimized, inexpensive, and support parallel query execution.
- Incorporate Hadoop, traditional data warehouse, and other data stores.
- Includes multiple repositories may reside in different locations.
- Include Data from mobile devices, sensors, cloud and the Internet of Things.
- Includes structure/semi-structured/unstructured, raw data.
- Inexpensive commodity hardware in cluster mode.
How Modern Data Warehouse Works?
Multiple Parallel Processing (MPP) Architectures
- MPP architecture enables a mighty scale and Distributed Computing.
- Resources add for a linear scale-out to the largest Data Warehousing projects.
- Multiple parallel processing architecture uses a “shared-nothing”. There are numerous physical nodes, each runs its instance. This results from performance many times faster than traditional architectures.
Multi-Structured Data
- Define Big Data & Analytics Infrastructure for multiple storage data with a polyglot persistence strategy.
- Integrate portions of the data into the Data Warehouse.
- Federated query access.
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