If data is the new corporate currency, it is worthless without the effective use of analytics to extract that value, and yet, some 59 percent of organizations believe they lack the capabilities to generate meaningful business insights from their data.
More than 80 percent of enterprise data is unstructured, which is the information that resides in emails, documents or files, for example. And yet, almost 80 percent of enterprises have little to no visibility of their unstructured data.
The other obvious challenge is simply storing and analyzing all this information, particularly as the amount of information stored in the world’s IT systems is doubling roughly every two years.
Big Data processing is a relatively new field and, as a result, very few experts are available. By 2018, the U.S. alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of Big Data to make effective decisions.
Big Data is just getting started. If you think you’ve got a lot of data to manage now, the rapid growth in adoption of the Internet of Things (IoT), machine learning and e-commerce will only bring more in the future.
This is where Big Data processing must step up to the plate to give businesses the right tools for the job. Alibaba Cloud is actively supporting organizations to overcome this challenge with a powerful product called AnalyticDB, which is a real-time Online Analytical Processing (OLAP) managed database cloud service for crunching enormous amounts of data.
Before we look at the features of AnalyticDB, it’s important to first distinguish between Online Analytical Processing (OLAP) and Online Transaction Processing (OLTP).
In order to access and record information, databases typically store data processed in real-time, which is known as online transaction processing (OLTP). Meanwhile, OLAP, also known as an Enterprise Data Warehouse (EDW) system, stores data for the purpose of future analysis and is optimized for storing data and then performing offline analytics and creating reports.
An example of an OLTP task would be using SQL to retrieve information to process a purchase order. An e-commerce store, for instance, would need access to multiple tables in real-time to process orders, including customers’ billing information, mailing address, and the business’ inventory list. Rather than crank up a server and wait minutes to retrieve the data needed to process an order, SQL can be used to access that information and perform online transaction processing (OLTP) in real-time.
The role of OLAP is to then analyze tables of information and transactions after the purchase has taken place. This may mean analyzing transactions in relation to other transactions, i.e. what did other customers buy? Or analyzing commonalities among customer types, i.e. where they live and what time they make orders?
Ultimately, the goal of OLAP is to store data that will be analyzed later and create insight to empower decision-makers and optimize operations.
This brings us to Alibaba Cloud AnalyticDB, which integrates into your business ecosystem and equips you with the power to discover new trends through data analysis. Based on a highly available distributed system, AnalyticDB offers high-performance computing analysis of massive amounts of data, which wouldn’t be possible with a traditional OLAP engine. This includes the ability to obtain data insights sourced from multiple locations and instant processing of data in a highly concurrent and multi-dimensional analysis system.
There is no upfront costs or investment to procure equipment when using AnalyticDB as the service is hosted on the cloud and charged on a Pay-As-You-Go basis.
Below is a full comparison of AnalyticDB compared to traditional OLAP engines:
| Parameter | AnalyticDB | Traditional OLAP Engines |
|---|---|---|
| Data usage | Supports business-driven in-depth data exploration and interactive data analysis. | Most engines primarily support data-driven computing modes for fixed report-type presentations. |
| Data scale | The high-performance computing engine supports computing and analysis of massive data. | The low-performance computing engine is able to support only a portion of the data for computation. |
| Engine capabilities | Powered by distributed computing technology. Results can be computed quickly allowing free data exploration. Typically, no advance modeling is required. | In most situations either query speeds are slow, or you must establish a cube or other models in advance and analyze the data based on this model. |
| Cost | Under the Pay-As-You-Go model, you are only charged for the resources you actually use. | Works on one-time payment or license purchase models. |
| Delivery Methods | 1. Ready for use right after you apply. 2. Provides multi-tenant flexibility. 3. Database provisioning with a few clicks instantly. |
In regards to practical application, AnalyticDB is suitable for frontend service systems which embed big data queries to gain insightful data for business processes.
For example, AnalyticDB can be used for running centralized business intelligence (BI) models, enterprises systems such as Customer Relationship Management (CRM) systems, Data Management Platforms (DMP), data report products, and other SaaS projects.
For offline computing or for stream computing, you can link the service with Alibaba Cloud MaxCompute, which is Alibaba Cloud’s platform for large-scale data warehousing. MaxCompute is different from other Big Data platforms as it is AI-enabled, guarded by Alibaba Cloud security systems and provides peak performance at a low cost. The platform offers a combination of data intelligence services, mainly for batch structural data storage and processing. It can process 100PB data in six hours, which is roughly the same amount of data as 100 million HD movies, or one-third of Facebook’s entire 300PB data warehouse.
Visit Alibaba Cloud to learn more about AnalyticDB and MaxCompute,
