Real-time Data Processing
Hologres supports real-time data writes and updates. Data can be queried immediately after it is written. It is natively integrated with Flink and supports the development of real-time data warehouses with high throughput, low latency, and models to meet the requirements for real-time business insights.
Interactive Analysis in Sub-seconds
Hologres supports interactive analysis in sub-seconds for large volumes of data, without the need for pre-computing. It also supports diversified types of data analysis features, such as multidimensional analysis, ad hoc analysis, exploratory analysis, and accelerated queries on MaxCompute data, to meet your requirements for data analysis based on what you see is what you get (WYSWYG).
Centralized Data Serving
Hologres supports multiple scenarios such as multidimensional analysis, high-performance point queries, and data retrieval. It supports load isolation, simplifies data architecture, unifies data access interfaces, and implements Hybrid Serving & Analytics Processing (HSAP).
Hologres supports the standard SQL protocol and can seamlessly connect to mainstream BI and SQL development frameworks. You do not need to rewrite your applications. Hologres also supports data lakes and semi-structured data, such as JSON data. You can import data from Object Storage Service (OSS) or Data Lake Formation (DLF) to Hologres with ease.
Queries and Analysis in Multiple Scenarios
Hologres stores data in column- or row-oriented mode. It also allows you to conduct simple, complex, or ad-hoc queries. Hologres can process a large volume of SQL statements concurrently in distributed mode. It returns query responses on large volumes of data at sub-second speeds. The resource utilization is high.
Complex Query on Big Data
Hologres supports parallel computing and responds to association analysis of PB-scale data at sub-second speeds.
Point Query on Big Data
Hologres stores PB-scale data and writes or queries 100 million records per second. This is more than 10 times the performance of open source components.
Hologres seamlessly connects to MaxCompute. You can use Hologres to perform interactive analysis without the need to move data. Query results are quickly generated. You can use Hologres to query MaxCompute data or implement federated computing based on MaxCompute data and real-time data.
Cloud-native Real-time Data Warehouse
Based on the characteristics of a real-time data warehouse, including frequent data updates, agile processing, and flexible and self-service analysis, Hologres supports real-time writes and updates at high concurrency, transaction isolation and atomicity, data can be queried immediately after it is written.
Real-time High-throughput Data Writes and Updates
Hologres is integrated with computing frameworks such as Flink and Spark. It allows you to use built-in connectors to write and update large volumes of data in real time. You can use various tables such as source tables, result tables, and dimension tables, and perform complex operations, such as merging multiple data streams.
Hologres allows you to query data the moment after it is written. You can query data in a specific table or all tables in a schema or database. It also allows you to create, delete, or update a view for one or more tables. In addition to data update and delete operations, you can join tables, perform nested queries, and use window functions to query data in Hologres. Hologres also supports semi-structured JSON data.
End-to-End Event Driven
Hologres allows you to parse the binary logs of table update events. You can use Flink to consume Hologres binary logs in order to realize end-to-end real-time development across warehouse layers. This way, you can reduce the end-to-end latency of data processing while meeting the requirements for tiered data governance.
Hologres uses an architecture in which computing and storage resources are separated. It supports fine-grained control such as the management of computing loads and access permissions. It provides rich monitoring and alerting features, supports hot system updates, and meets enterprise-level security and reliable O&M requirements.
Hologres provides fine-grained access control policies and data security features, including Bring Your Own Key (BYOK) encryption, data masking, Data Security Guard, and IP address whitelists. It also supports multiple authentication systems such as Resource Access Management (RAM), Security Token Service (STS), and independent account systems. Hologres has passed Payment Card Industry Data Security Standard (PCI DSS) assessment.
Hologres supports the isolation of loads based on resource groups. This allows you to isolate resources for different business requirements, various query types, and data reads and writes. This way, you can ensure the sustainability and stability of the system.
Hologres allows you to use multiple compute instances to build a highly reliable system. In high-reliability mode, Hologres supports storage sharing among compute instances, fault isolation, high availability of online services, and rapid automatic recovery of failed nodes. Storage and computing are separated, and resources are independently scaled. No local disks are required. The three-replica redundant storage of Apsara Distributed File System ensures high reliability.
Ecosystem and Scalability
Hologres is compatible with the PostgreSQL ecosystem and seamlessly integrated with DataWorks, which is the big data compute engine and big data development platform of Alibaba Cloud. You can get started with Hologres without additional learning.
Compatibility with PostgreSQL
Hologres is compatible with the PostgreSQL ecosystem. It provides a JDBC or ODBC API to interconnect with third-party extract, transform, and load (ETL) and BI tools, such as Tableau, QuickBI, and PowerBI.
Integration with DataWorks
Hologres is seamlessly integrated with DataWorks to provide graphic, intelligent, and unified data warehousing and interactive analysis.
Built-in Vector Search Engine Proxima from DAMO Academy
Hologres is tightly integrated with Machine Learning Platform for AI (PAI). The built-in vector search engine Proxima from DAMO Academy supports real-time computing of feature data and real-time vector retrieval.
Pay-as-you-go applies to scenarios when computing or storage resources are unspecified.
You are billed based on the running durations of your instances, the size of stored data, and storage durations.
Subscription applies to scenarios when Hologres is used for real-time analysis in the long term.
You need to estimate the required computing and storage resources based on your actual business scenario and purchase this service in subscription mode.