How to Implement Big Data Projects on the Cloud

Cloud computing and big data are currently hot topics. How to combine the two to realize big data projects on the cloud is a new field of practice. Big data analytics is the often complex process of examining big data to uncover information. Based on his own experience, data expert David Gillman enumerated the basic elements that need to be considered in cloud big data solutions.

In addition, this includes building real-time indexing of data, free-mode search and analysis, monitoring data and providing real-time warnings, etc., to help users better evaluate and Choose a solution .

When talking about how to implement big data projects on the cloud, David emphasized three real-time elements, namely real-time indexing, real-time data, and real-time monitoring. Specifically, real-time indexing refers to “creating a universal real-time index for all machine data”:

This is what most people think of as the core of big data; it is often equivalent to the open source project Hadoop. The company may have been overwhelmed by requests from radio frequency ID (RFID) moves, website clicks and other potentially structured data. If you know how this data will be used and how to query and access it in the future , then it is worth the investment in processing this data.

You don’t need to know the potential future use of the data, Hadoop provides a solution. By getting the incoming data as-is, Big Data defers the data definition step until the analysis is performed. Without limiting the future use of data, Hadoop distributes data across many servers and keeps track of the data location.

Real-time data refers to “free search and analysis of real-time data and historical data”, and storing data is only part of the road to achieving goals. Another aspect is that information needs to be found relatively easily. To this end, the fastest way is to provide a quick (in terms of implementation, not response time) search function.

Therefore, it is necessary to find tools that support text search on unstructured data. Obtaining the response directly from the monitoring program can make people vaguely believe that all information is stored correctly and accessible. The management step of this process is to index the data content stored in distributed nodes. Search queries and then access indexes on distributed nodes in parallel to provide faster response.

Real-time monitoring refers to “monitoring data and providing real-time warnings”:

Look for a tool to monitor the data in big data. Some tools can create queries that are continuously processed, looking for conditions to be met. I cannot list all possible uses for real-time monitoring of data entering Hadoop. Assuming that most of the incoming data is unstructured data and is not suitable for relational databases, then real-time monitoring may be a way to carefully examine the data elements.

In addition to the three “real-time”, Daivid also listed seven other points, which can be summarized as:

Automatically discover valid information from data

 Performing manual searches and manual reports can also affect analysis efficiency. Data mining and predictive analysis tools are rapidly developing in the following directions: Big data can be used as a database for analyzing data sources, or as a database for continuously monitoring changes.

All data mining tools follow this goal. Someone determines the purpose of the analysis, looks at the data, and then develops statistical models that provide insights or predictions. Then, these statistical models need to be deployed in a big data environment to perform continuous evaluation. This part of the operation should be automated.

Provide powerful specific reports and analysis

Similar to knowledge discovery and automated data mining, analysts need to gain access to retrieve and summarize information in the big data cloud environment. The number of vendors with big data reporting tools seems to be increasing every day.

Cloud-based big data providers should support both Pig and HQL statements from external requesters. In this way, big data storage can be queried by people using tools of their choice (or even tools that have not yet been created).

Provides the ability to quickly build custom dashboards and views

Like the evolution of traditional business intelligence projects, when people can query big data and generate reports, they want to automate the function and create a dashboard for repeated viewing through beautiful pictures. Unless people write their own Hive statements and only use Hiveshell, most tools have the ability to use query statements to create dashboard-like views.

It is too early to list many examples of dashboards in big data deployments. A prediction based on the history of business intelligence is that dashboards will become an important internal delivery tool for aggregated big data. And from the historical development of business intelligence, having a good big data dashboard is essential for obtaining and maintaining senior leadership support.

Use ordinary hardware for efficient expansion to support any amount of data

When using cloud big data services, this consideration has little practical significance. It is the responsibility of the service provider to purchase, equip and deploy the hardware used to store data. The choice of hardware should not be difficult.

However, the good news is that the bill shows that big data is suitable for ordinary hardware. On some nodes in the architecture, “high-quality” servers are useful. However, most of the nodes (nodes that store data) in the big data architecture can be placed on “lower quality” hardware.

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Provide fine-grained, role-based security and access control

When unstructured data is in relational data, the complexity of accessing the data may hinder people from obtaining the data. Common reporting tools do not work. Considering the use of big data is an effective step to simplify complex access.

Unfortunately, the same security settings usually cannot be migrated from existing relational systems to big data systems. The more big data you use, the more important it becomes for good security. At first, there may be very little security protection, because no one knows how to handle big data.

As companies develop more analytics that use big data, results (especially reports and dashboards) need to be protected, similar to protecting reports from current relational systems. Start using cloud-based big data and understand when security needs to be applied.

Supports multi-tenancy and flexible deployment

The use of cloud brings the concept of multi-tenancy, but this is obviously not a consideration in the internal big data environment. Many people feel uneasy about putting critical data in a cloud environment. And importantly, the cloud provides the low-cost and rapid deployment needed to begin big data projects.

It is precisely because cloud providers place data in an architecture with shared hardware resources that costs can be significantly reduced. God is fair, it’s okay to put the data on your server and let someone else manage the entire setup.

However, this is not a cost-effective business model when big data needs are intermittent. The result will be higher expenses, because the company will pay for a large amount of free time, especially during the realization of projects, when analysts explore, consider and understand big data.

Integrate APIs and extend through them

Big data is designed for access by custom applications. The common access method uses RESTful application programming interface (API). These APIs can be used in every application in the big data environment for administrative control, data storage, and data reporting.

Because all the basic components of big data are open source, these APIs are fully explained and can be widely used. Finally, it is hoped that cloud-based big data providers will allow access to all current and future APIs with appropriate security protection.

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