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Data Warehouse Migration
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Converged Big Data Analysis
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ETL + BI Analysis
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Real-Time Data Analysis
Data Warehouse Migration
The data warehouse is an important data analysis system for enterprises. As the service volume grows, performance of self-built data warehouses cannot meet the actual service requirements due to their poor scalability and high costs. As an enterprise-class data warehouse on the cloud, DWS features high performance, low cost, and easy scalability, satisfying requirements in the big data era.
Advantages
Seamless Migration
DWS provides various migration tools to ensure seamless migration of popular data analysis systems such as TeraData, Oracle, MySQL, SQL Server, PostgreSQL, Greenplum, and Impala.
Compatibilities with Traditional Data Warehouses
DWS supports the SQL 2003 standard and stored procedures. It is compatible with some Oracle syntax and data structures, and can be seamlessly interconnected with common BI tools, smoothing service migration efforts.
High Security and Reliability
DWS supports data encryption and connects to the Database Security Service (DBSS) to ensure data security on the cloud. In addition, DWS supports automatic full and incremental backup of data, improving data reliability.
Converged Big Data Analysis
Data has become the most important asset. Enterprises must be able to integrate their data resources and analysis platforms with high precision to mine the full value of their data. In predictive analysis use cases, massive volumes of data must be processed. Huawei DWS delivers the needed processing power to handle these intense compute scenarios.
Advantages
Unified Analysis Entry
The DWS SQL serves as the unified entry of upper-layer applications, so that application developers can access all data using the SQL.
Real-Time Interactive Analysis
Analysis personnel can obtain immediately-actionable information from the big data platform in real time using broad analysis requests.
Auto Scaling
Adding nodes allows you to easily expand into PB-range capacity while enhancing query and analysis performance of the system.
Enhanced ETL + Real-Time BI Analysis
The data warehouse is the pillar of the BI system for collecting, storing, and analyzing massive volumes of data. It powers business decision analysis for the IoT, finance, education, mobile Internet, and O2O industries.
Advantages
Data Migration
Ability to import data in batches in real time from multiple data sources.
High Performance
Cost-effective PB-level data storage and response to correlation analysis of trillions of data records within seconds.
Real-Time
Real-time consolidation of service data to produce actionable insights in operational decision-making.
Real-Time Data Analysis
In the mobile Internet and IoT domains, huge volumes of data must be processed and analyzed in real time to extract the full data from data. The quick data import and query capabilities of DWS accelerate data analysis capabilities to enable real-time ingestion, processing, and value generation.
Advantages
Real-Time Import of Stream Data
Data from IoT and Internet applications can be written into DWS in real time after being processed by the stream computing and AI services.
Real-Time Monitoring and Prediction
Device monitoring, control, optimization, supply, self-diagnosis, and self-healing based on data analysis and prediction.
Converged AI Analysis
Correlation analysis can be conducted on results of image and text data analysis by AI services and other service data on DWS.
Functions
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High Reliability
Provides various functions to improve the reliability of data warehouse clusters.
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PB-Level High-Performance
Provides high-performance data warehouses dedicated for enterprises.
Built-in redundancy at the instance and data-level avoids potential single points of failure (SPOFs), adding robustness to the service mix.
Provides two data replicas and supports manual data backup to OBS.
Automatically isolates faulty nodes, uses the replica to recover data, and supports node replacement.
Automatic incremental backup ensures data reliability at zero cost.
Uses a MPP-based computing framework, and supports row and column-based data storage and vectorized executors, as well as SQL 03 analysis functions.
Adopts in-memory computing, MPP and SMP technologies, and LLVM, improving performance by as much as 10 times.
Optimizes the communication between large-scale clusters based on telecommunication technologies, improving data transmission efficiency between compute nodes.
Cost-based optimizer generates an optimal execution plan based on cluster scale and data volume for supercharged efficiency.
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Openness and Compatibility
Supports comprehensive SQL capabilities and smooth application migration.
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Monitoring and Audit
Controls service usage and status.
Supports SQL 92 and SQL 2003 standards, complete transaction processing capabilities, and stored procedures.
One-click schema migration tool and support for SQL code conversion for heterogeneous databases like Oracle and MySQL.
Compatible with the PostgreSQL ecosystem and supports interconnection authentication with mainstream database ETL and BI tools provided by third-party vendors.
Compatible with Oracle, PostgreSQL, and Teradata.
Integrates with CTS to allow you to audit all operations performed on the DWS management console and API invocation.
Integrates with Cloud Eye to allow you to monitor compute nodes and databases in the cluster.
Records all SQL operations, involving connection attempts, queries, and database changes.
