New Product Launch: Alibaba Cloud Data Integration

简介: Support online real-time & offline data exchange between all data sources, networks and locations with Alibaba Cloud Data Integration.

Big Data is the new corporate currency. If used correctly, there is immense value to be extracted. Revenues from Big Data services, software and hardware are predicted to reach USD $187BN in 2019, representing an increase of more than 50 percent over a five-year period.

Much of this data will pass through the cloud, with 50 percent of organizations predicted to embrace a cloud-first policy
in 2018 for Big Data and analytics. Enterprises are clearly demanding more flexibility and control over costs than on-premises solutions can deliver.

As the maturity of cloud-based technologies and the surge of Big Data converge, it is impossible to ignore the competitive edge a data processing and warehousing solution that is infinitely scalable and equally elastic brings to the enterprise. The tipping point for Big Data is here.

But why are we seeing a surge in cloud demand now? One major reason is the fact that technologies powering the cloud have not just increased in sophistication but concerns about security in the cloud have also diminished.

From complex, secured APIs to robust authentication and best practices, cloud platforms are investing in a range of features and support to ensure greater security and scalability. This strategy is paying off with the total number of organizations who distrust cloud dropping from 50 percent to 29 percent within just 12 months.

As a major cloud and big data infrastructure provider, Alibaba Cloud provides an expanding suite of cloud-based products to manage commercial big data problems, including Alibaba Cloud Data Integration, which has just recently been launched for the international market.

Data Integration is an all-in-one data synchronization platform that supports online real-time and offline data exchange between all data sources, networks, and locations. Based on an advanced distribution architecture with multiple modules (such as dirty data processing and flow control distributed system), the service provides data transmission, data conversion and synchronization services. It also supports multiple features, including support for multiple data sources, fast transmission, high reliability, scalability, and mass synchronization. Below we will take a closer look at the features and benefits of this new product and how your organization can add Data Integration to fulfill your Big Data processing needs.

Support for Multiple Disparate Data Sources

Data Integration supports data synchronization between more than 400 pairs of disparate data sources (including RDS databases, semi-structured storage, non-structured storage (such as audio, video, and images), NoSQL databases, and big data storage). This also includes important support for real-time data reading and writing between data sources such as Oracle, MySQL, and DataHub.

Scheduled Tasks

Data Integration allows you to schedule offline tasks by setting a specific trigger time (including year, month, day, hour, and minute). It only requires a few steps to configure periodical incremental data extraction. Data Integration works perfectly with DataWorks data modeling. The entire workflow is an integration of operations and maintenance.

Mass Upload to Cloud

Data Integration leverages the computing capability of Hadoop clusters to synchronize the HDFS data from clusters to MaxCompute, known as Mass Cloud Upload. Data Integration can transmit up to 5TB of data per day and the maximum transmission rate is 2GB/s.

Monitoring and Alarms

With 19 built-in monitoring rules, Data Integration applies to most monitoring scenarios. You can set alarm rules based on these monitoring rules. Additionally, you can pre-define the task failure notification mode for Data Integration.

Data Source Management

By leveraging the data sources and datasets that define the source and destination of data, Data Integration provides two data management plug-ins. The Reader plug-in is used to read data and the Writer plug-in is used to write data. Based on this framework, a set of simplified intermediate data transmission formats is developed to exchange data between arbitrary structured and semi-structured data sources.

Local Data Collection

Data Integration supports data synchronization in Alibaba Cloud classic networks and VPCs (virtual private cloud), as well as data collection in local IDCs.

Full Database Migration

Data Integration provides a full database migration tool which allows the creation of multiple data synchronization tasks and imports all data tables in a MySQL database to MaxCompute. By using full database migration, you no longer need to create synchronization tasks one at a time.

Incremental Synchronization

By using the WHERE clause, Data Integration supports business data filtering by date. Data with different dates is synchronized to the relevant MaxCompute partition tables. By setting the synchronization interval to 1 hour or 10 minutes, Data Integration is capable of performing quasi-real-time incremental synchronization.

To learn more about Data Integration, visit the product page at Alibaba Cloud today.

目录
相关文章
|
druid 前端开发 关系型数据库
mysql使用druid时自动断开连接解决方案
mysql使用druid时自动断开连接解决方案
386 0
|
Linux 应用服务中间件 nginx
【PUSDN】centos查看日志文件内容,包含某个关键字的前后5行日志内容,centos查看日志的几种方法
【PUSDN】centos查看日志文件内容,包含某个关键字的前后5行日志内容,centos查看日志的几种方法
246 0
|
缓存 边缘计算 负载均衡
如何理解CDN?它的实现原理是什么?
如何理解CDN?它的实现原理是什么?
1172 0
|
资源调度 流计算
Flink 指标参数源码解读(读取数量、发送数量、发送字节数、接收字节数等)(下)
Flink 指标参数源码解读(读取数量、发送数量、发送字节数、接收字节数等)(下)
226 1
|
6月前
|
存储 关系型数据库 MySQL
double ,FLOAT还是double(m,n)--深入解析MySQL数据库中双精度浮点数的使用
本文探讨了在MySQL中使用`float`和`double`时指定精度和刻度的影响。对于`float`,指定精度会影响存储大小:0-23位使用4字节单精度存储,24-53位使用8字节双精度存储。而对于`double`,指定精度和刻度对存储空间没有影响,但可以限制数值的输入范围,提高数据的规范性和业务意义。从性能角度看,`float`和`double`的区别不大,但在存储空间和数据输入方面,指定精度和刻度有助于优化和约束。
939 5
|
9月前
ARM64技术 —— 系统调用指令SVC、HVC和SMC的使用规则
ARM64技术 —— 系统调用指令SVC、HVC和SMC的使用规则
|
9月前
|
运维 监控 安全
携手阿里云CEN:共创SD-WAN融合广域网
在9月19日的阿里云云栖大会上,犀思云与阿里云联合推出Fusion WAN智连阿里云解决方案,该方案深度融合阿里云网络产品,如CEN和TR,实现一键部署和简化操作,大幅提升企业上云的可靠性和安全性。此创新方案不仅展示了犀思云在SD-WAN领域的技术积累,也体现了双方在推动企业数字化转型方面的强大实力,为客户带来更灵活、更高效的上云体验。
223 16
携手阿里云CEN:共创SD-WAN融合广域网
|
机器学习/深度学习
探索Transformer在金融行情预测领域的应用——预测银行间回购市场加权价格
文章先发在公众号上来,顺便在这里也写一下,主要思路其实就是模仿盘古天气大模型的方法,来试试能不能用来预测全国银行间市场质押式回购每日的加权平均价格,目前模型主要架构和训练粗略的跑了出来,效果不是太好,目前看了点其他paper,希望尝试利用已经开源的各种大模型做微调。欢迎大家批评指正。
探索Transformer在金融行情预测领域的应用——预测银行间回购市场加权价格
|
12月前
|
存储 SQL 数据库
数据库技术探索:基础架构、应用场景与未来展望
一、引言 数据库技术是信息时代的基石,为企业和组织提供了数据存储、检索、分析和管理的核心支撑
|
安全 Unix Linux
Linux系统之passwd命令的基本使用
Linux系统之passwd命令的基本使用
432 1

热门文章

最新文章

下一篇
DataWorks
AI助理

你好,我是AI助理

可以解答问题、推荐解决方案等