Why Do I Need to Use GaussDB(DWS)?
Background
Large amounts of data (orders, inventories, materials, and payments) are being generated in enterprises' business operation systems and transactional databases everyday.
Decision makers need to effectively harness and analyze this data to extract valuable insights that inform strategic decision-making and drive business success.
Challenges
A data classification and analysis task usually involves simultaneous access to data in multiple database tables, which means multiple tables that are possibly being updated by different transactions need to be locked at the same time. This can be quite difficult for a busy database system.
- Locking multiple tables increases the latency of a complex query.
- Blocking transactions that are updating the database tables causes increased latencies or even interruptions for these transactions.
Solutions
Data warehouses excel in data aggregation and association, facilitating large-scale data mining for better decision-making support. Data mining requires complex queries that involve multiple tables.
The ETL process copies data from dedicated business databases to a data warehouse for centralized analysis and computing. Data of multiple systems can be aggregated to one data warehouse for the extraction of more valuable data insights.
Data warehouses are designed differently from standard transaction-oriented databases, such as Oracle, SQL Server, and MySQL. Data warehouses are optimized in terms of data aggregation and association, but certain features of standard databases, such as transactional properties and data manipulation operations (add, delete, modify) may be compromised or become unavailable. This is why data warehouses and databases are usually used for different purposes. Transactional databases focus on online transaction processing, while data warehouses are better at complex queries and analysis. They perform their own duties and do not interfere with each other. You could also say databases are for data updates whereas data warehouses are for data analysis.
Cloud Data Warehouse Solution
Conventional data warehouses are not a feasible option for smaller enterprises due to high cost, time-consuming selection and procurement of hardware, and complex capacity expansion.
GaussDB(DWS) offers a better choice:
- This cloud-based, distributed MPP data warehousing service is open, efficient, compatible, scalable, and easy to maintain.
- Built on the GaussDB data warehouse kernel, it provides a seamless experience for enterprises across both on-cloud and on-premises environments.
GaussDB(DWS) is a next-generation distributed data warehousing system with independent intellectual property rights and better guarantee of business continuity. Currently, it is widely used in government, finance, and carriers. FusionInsight LibrA is compatible with mainstream open-source Postgres databases, especially in Oracle and Teradata SQL statements. GaussDB(DWS) engineers have designed a kernel of hybrid row-column stores not only for faster analysis but also for data processing, such as adding, deleting, modifying data. FusionInsight LibrA features the cost optimizer and warehouse technologies, including machine code vector computing and inter/intra-parallelism for operators and nodes. It uses LLVM to optimize the local code in compilation query plans. More powerful data query and analysis addresses service pain points and improves user experience.
- GaussDB(DWS) can be used out of the box.
Enabling GaussDB(DWS) on the cloud platform takes only a few minutes, freeing you from the time consuming process of searching for and purchasing data warehouses. This not only simplifies the procurement, but also lowers the cost and requirements for using data warehouses. Small and medium-sized enterprises with access to GaussDB(DWS) can seamlessly mine large amounts of data for actionable insights.
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