Top 8 Data Warehouse Tools
SnowflakeOracle ExadataVerticaTeradataMicrosoft Parallel Data WarehouseApache HadoopOracle Database ApplianceSAP BW4HANA
All the people who are working with Snowflake are extremely happy with it because it is designed from a data-warehousing point of view, not the other way around. You have a database and then you tweak it and then it becomes a data warehouse.
On-premises Exadata is just as stable as the cloud version. It's a very stable platform.
The offloading of data to the SIM is a valuable feature.
Vertica is a columnar database where the query performance is extremely fast and it can be used for real-time integrations for API and other applications. The solution requires zero maintenance which is helpful.
Teradata has good performance, the response times are very fast. Overall the solution is easy to use. When we do the transformation, we have all of our staging and aggregation data available.
Tools like the BI and SAS are excellent.
It is not a pricey product compared to other data warehouse solutions.
Hadoop is extensible — it's elastic.
Hadoop is designed to be scalable, so I don't think that it has limitations in regards to scalability.
If the database goes down on one of the servers, the load is automatically shifted to another server. You can troubleshoot the one that is down without affecting uptime.
I like that it's quite quick.
Some of the main features of this solution are that it uses HANA and it has good performance.
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).