AnalyticDB for MySQL is a fully managed data warehouse service that can process petabytes of data in real time. AnalyticDB for MySQL is highly compatible with MySQL and can update data within milliseconds and respond to queries within sub-seconds.
AnalyticDB for MySQL utilizes a data lakehouse architecture to process structured, semi-structured, and unstructured data from data warehouses and data lakes in an efficient manner and build a comprehensive data analysis platform for enterprises. AnalyticDB for MySQL supports batch processing of large amounts of data to meet deep insight requirements and provides high-performance, real-time data analysis capabilities to help enterprises quickly respond to business changes, reduce costs, and improve efficiency.
What AnalyticDB for MySQL can do for you
Imports structured, semi-structured, and unstructured data to AnalyticDB for MySQL for multi-source integration and analysis.  | Synchronizes data from a variety of data sources, such as PolarDB, ApsaraDB RDS, ApsaraMQ for Kafka, and Simple Log Service, to AnalyticDB for MySQL in real time.  |
Batch extract, transform, load (ETL) processing Extracts data from data sources, cleanses and transforms the data, and then loads the data into AnalyticDB for MySQL. Scheduling tools, such as Data Management (DMS), DataWorks, Airflow, DolphinScheduler, and Azkaban, can be used for periodic ETL processing.  | AnalyticDB for MySQL integrates the Spark compute engine. You can use Spark SQL to query structured data, Spark JAR packages to develop complex batch processing jobs, or PySpark to perform machine learning and data computation.  |
Why select AnalyticDB for MySQL
Support for various data sources
AnalyticDB for MySQL supports various data sources, such as relational databases, NoSQL databases, big data platforms, storage, logs, message queues, and text files.
Type | Data source |
Relational database | ApsaraDB RDS for MySQL |
ApsaraDB RDS for SQL Server | |
PolarDB for MySQL | |
PolarDB-X | |
Self-managed MySQL database | |
Self-managed Oracle database | |
Non-relational database | ApsaraDB for MongoDB |
Lindorm | |
Self-managed HBase database | |
Big data | MaxCompute |
Flink | |
Hive | |
Storage | Object Storage Service (OSS) |
AWS S3 | |
Azure Blob Storage | |
Google Cloud Storage | |
Tablestore | |
HDFS | |
Log | Simple Log Service |
Logstash | |
Message queue | Kafka |
On-premises file | Text file |
For information about how to import data from a data source to AnalyticDB for MySQL, see Data import.
Seamless integration with multiple clients, drivers, BI tools, and scheduling tools
AnalyticDB for MySQL is compatible with MySQL and can be seamlessly integrated with most clients, drivers, business intelligence (BI) tools, and scheduling tools that support MySQL.
Category | Tool |
Client | |
Driver | |
BI tool | |
Scheduling tool |
|
High performance
AnalyticDB for MySQL can handle operations on large amounts of data within seconds or milliseconds, delivering a performance for complex queries that is 10 times faster than relational databases.
In AnalyticDB for MySQL, data becomes queryable milliseconds after it is written, even in large amounts, and strong data consistency is ensured.
Elastic scaling
AnalyticDB for MySQL uses a storage-compute decoupled architecture and allows compute resources and storage resources to be separately scaled.
To handle business fluctuations, you can manually scale compute resources or configure automatic elastic scaling within specific compute resource specifications.
Storage resources can be automatically scaled based on the data volume. You are charged for storage resources on a pay-as-you-go basis. You can also store historical data in OSS to reduce costs.
Data security and compliance for enterprises
AnalyticDB for MySQL ensures data security and compliance of enterprises in various aspects such as permissions, connections, encryption, audit, and backups. The following table describes some features of AnalyticDB for MySQL in security and compliance.
Category | Feature | Description |
Permission management | After you use an Alibaba Cloud account to grant permissions to a RAM user, the RAM user can create and manage AnalyticDB for MySQL clusters based on the permissions. For example, you can log on to the AnalyticDB for MySQL console, create or delete a cluster, and configure an IP address whitelist as the RAM user. | |
AnalyticDB for MySQL supports permissions at the global, database, table, and column levels and allows you to grant different levels of permissions to database accounts. | ||
Data security | By default, AnalyticDB for MySQL denies access from all IP addresses to ensure security and stability. Before you connect to an AnalyticDB for MySQL cluster from a client, you must add the IP address of the client to a whitelist of the cluster. | |
To improve data transmission security, you can enable the SSL encryption feature and install SSL certificates that are issued by certificate authorities (CAs) to the required applications. The SSL encryption feature encrypts connections at the transport layer to improve data security, ensure data integrity, and prevent data listening, interception, and tampering. | ||
After you enable the disk encryption feature for an AnalyticDB for MySQL cluster, AnalyticDB for MySQL encrypts data on each data disk of the cluster based on block storage. This way, the data cannot be decrypted even if data leaks occur. | ||
AnalyticDB for MySQL provides the SQL audit feature to log DML and DDL operations in real time. This helps you identify abnormal SQL queries and resolve database performance issues. | ||
Backup and restoration | After you create an AnalyticDB for MySQL cluster, AnalyticDB for MySQL enables the data backup feature for the cluster to perform periodic data backup. | |
AnalyticDB for MySQL allows you to restore data from backup sets to new clusters. | ||
Monitoring and alerting | AnalyticDB for MySQL allows you to view cluster performance metrics within a specific time range in the AnalyticDB for MySQL console or by calling API operations, such as the CPU utilization, compute memory usage, disk usage, and response time. | |
AnalyticDB for MySQL allows you to view Spark performance metrics in the CloudMonitor console or by calling API operations. | ||
AnalyticDB for MySQL allows you to configure alert rules. If an alert threshold is reached, AnalyticDB for MySQL notifies alert contacts to ensure that issues are resolved at the earliest opportunity. |
For more information, see Functions and features.
Pricing
AnalyticDB for MySQL fees consist of reserved resource fees, elastic resource fees, data storage fees, cache storage fees, and backup storage fees.
For more information about billable items, see Billable items of Enterprise Edition and Basic Edition.
For more information about prices of billable items, see Pricing for Enterprise Edition and Basic Edition.
Create a cluster
AnalyticDB for MySQL is available in Enterprise Edition and Basic Edition. The two editions provide the same features but are different in the storage architecture. Enterprise Edition contains reserved nodes in multiples of 3 on a multi-replica architecture and is suitable for production environments. Basic Edition contains only one reserved node on a single-replica architecture and is suitable for learning and testing environments.
Create an Enterprise Edition or Basic Edition cluster to experience AnalyticDB for MySQL.
Getting started
You can get started with AnalyticDB for MySQL by reading the following tutorials based on your user roles.
Database administrators
Manage database accounts and permissions
Manage IP address whitelists for database access
Audit DML and DDL operations
Configure backup cycle and frequency to prevent data loss
Data development engineers
Migrate or synchronize data to data warehouses or data lakes
Write code to connect to AnalyticDB for MySQL and execute complex data processing jobs
Design table schemas to improve query performance
Use Spark SQL statements or Spark applications to perform data cleansing, transformation, and computation
Data analysts
Use BI tools to build visualization dashboards
Use SELECT statements to query data
Use functions to process and analyze data
Use full-text search to perform fuzzy match and similarity search
Algorithm engineers
Use PySpark to perform preprocessing, cleansing, transformation, joins, and unions for large amounts of data