Top 8 Data Warehouse
SnowflakeOracle ExadataTeradataVerticaOracle Database ApplianceApache HadoopSAP BW4HANAIBM Netezza Performance Server
I like Snowflake's data exchange capabilities. It can exchange data with downstream systems and other vendor partners as well.
This is the advanced version of the cloud version, so it's really a flexible tool. If you have it implemented at home, you can access it from anywhere.
We have used this solution for a long period of time so it has become easy for us to query any kind of data from Oracle Exadata which has been valuable.
Teradata's most valuable feature is that it's easy to use.
I've never had any issues with scalability.
The most valuable feature is Vertica's performance and the ease of using the database.
The most valuable feature of Vertica is the ability to receive large aggregations at a very quick pace. The use case of subclusters is very good.
Oracle Database Appliance is a stable solution. We have clients that have been running it for 10 years.
The initial setup was straightforward and quick - I was able to configure everything within an hour.
The most important feature is its ability to handle large volumes. Some of our customers have really large volumes, and it is capable of handling their data in terms of the core volume and daily incremental volume. So, its processing power and speed are most valuable.
You can do hierarchical alert slicing and dicing out-of-box, which is not available in other solutions. I haven't come across that in Oracle or any other software provider.
The most valuable features of the IBM Netezza Performance Server are the NPS server because of the reduced maintenance and overall good performance.
The performance is most important to me, and it helps our ability to make business decisions quickly.
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Data Warehouse Topics
How does a data warehouse work?What is data warehousing used for?What is the Difference Between a Data Warehouse and a Database?Types of Data WarehouseBenefits of a Data Warehouse
How does a data warehouse work?
A data warehouse serves as a central repository for information that flows into it from various databases. The data is then processed, standardized, and merged so that it can be accessed by users in spreadsheets, SQL clients, and business intelligence tools. Once all of the data is compiled in one place, organization executives can analyze it and mine the data for patterns that will assist in making business decisions.
What is data warehousing used for?
Data warehousing is used in many sectors, including:
- Airline industry - for operations purposes such as crew assignments, route profitability analysis, and frequent flyer programs.
- Banking - for managing resources, performance analysis, and market research.
- Healthcare - for generating patient treatment reports, strategizing and predicting outcomes, and sharing data with insurance companies and medical aid services.
- Hospitality industry - for designing and estimating advertising campaigns and promotions based on client travel patterns and feedback.
- Investment and insurance sector - for analyzing customer trends and tracking market movements.
- Public sector - for gathering of intelligence such as tax records and health policy records.
- Retail chains - for distribution and marketing, for tracking customer buying patterns and for determining prices.
- Telecommunications - for making sales and distribution decisions.
What is the Difference Between a Data Warehouse and a Database?
Data warehouses and databases are both used for storing data. A database is used to store a large amount of real-time information, such as which items are in stock or have been sold. It processes your company’s daily transactions via simple queries. A data warehouse (DW or DWH) compiles historical (not current) data from multiple sources within your organization, handling complex queries which are used to create and analyze reports and then extract insights and make business decisions.
Databases and data warehouses process data differently. Databases use OLTP (online transactional processing) to quickly update a large amount of simple online transactions. OLTP responds immediately and therefore is useful in processing real-time data. Data warehouses, on the other hand, use OLAP (online analytical processing) to analyze large amounts of data and find out trends from them, such as how much is sold each day.
Types of Data Warehouse
There are three main kinds of data warehouse:
1. Enterprise Data Warehouse (EDW). This is a centralized warehouse that offers a unified approach for representing and organizing data. It allows data to be classified according to subject and helps executives to make tactical and strategic decisions.
2. Operational Data Store (ODS). This database integrates data from various sources for operational reporting and decision-making, and complements the EDW.
3. Data Mart. This subset of the data warehouse is specially designed for use by a specific department within the business, such as sales or finance, and can collect data directly from the sources.
Benefits of a Data Warehouse
The benefits of a data warehouse include:
- Enhances the quality and consistency of data. Data in a data warehouse is converted into a consistent format. With data across the organization standardized, the data will be more accurate, which means decisions made based on it will be more solid.
- Saves time and money. A data warehouse preserves, standardizes, and stores data from various sources, which aids in consolidating and integrating the data. Company executives can also query the data in the data warehouse themselves without IT support, which saves time as well as money.
- Delivers enhanced business intelligence from multiple sources. In addition, data warehouses can be easily applied to all of your business’s processes, such as sales, market segmentation, inventory, and financial management.
- Assists with decision-making and forecasting, including identifying potential KPIs and gauging predicted results.
- Streamlines the information flow to all parties.
- Provides a competitive advantage by offering a holistic view of the company’s standing and allowing executives to evaluate risks and opportunities.
Generates a high ROI (return on investment).