Welcome!

SDN Journal Authors: Liz McMillan, Yeshim Deniz, Elizabeth White, Pat Romanski, TJ Randall

Related Topics: @DXWorldExpo, Java IoT, Microservices Expo, @CloudExpo, Apache, SDN Journal

@DXWorldExpo: Article

Database to Implement Big Data Real-Time Application

Database will be capable for real-time application if performance is improved

The Big Data Real-time Application is a scenario to return the computation and analysis results in real time even if there are huge amounts of data. This is an emerging demand on database applications in recent years.

In the past, because there wasn't a lot of data, the computation was simple, and few parallelisms, the pressure on the database wasn't great. A high-end or middle-range database server or cluster could allocate enough resources to meet the demand. Moreover, in order to rapidly and parallel access to the current business data and the historic data, users also tended to arrange the same database server for both the query analysis system and the production system. This way, the database cost could be lowered, the data management streamlined, and the parallelism ensured to some extent. We are in the prime time of database real-time application development.

In recent years, due to the data explosion, and more diversified and complex applications, new changes have occured to the database system. The obvious change is that the data is growing at an accelerated pace. Applications are increasingly complex, and the number of concurrent access makes no exception. In this time of big data, the database is under increasing pressure, posing a serious challenge to the real-time application.

The first challenge is the real-timeness. With the heavy workload on the database, the database performance drops dramatically, the response is sluggish, and user experience is going from bad to worse quickly. The normal operation of the critical business system has been affected seriously. The real-time application has actually become the half real-time.

The second challenge is the cost. In order to alleviate the performance pressure, users have to upgrade the database. The database server is expensive, so are the storage media and user license agreement. Most databases require additional charges on the number of CPUs, cluster nodes, and size of storage space. Due to the constant increase of data volume and pressure on database, such upgrade will be done at intervals.

The third challenge is the database application. The increasing pressure on database can seriously affect the core business application. Users would have to off-load the historic data from the database. Two groups of database servers thus come into being: one group for storing the historical data, and the other group for storing the core business data. As we know, the native cross-database query ability of databases are quite weak, and the performance is very low. To deliver the latest and promptest analysis result on time, applications must perform the cross-database query on the data from both groups of databases. The application programing would be getting ever more complex.

The fourth challenge is the database management. In order to deliver the latest and promptest analysis result on time, and avoid the complex and inefficient cross-database programming, most users choose to accept the management cost and difficulty increase - timely update the historic library with the latest data from the business library. The advanced edition of database will usually provide the similar subscription & distribution or data duplicate functions.

The real-time big data application is hard to progress when beset with these four challenges.

How to guarantee the parallelism of the big data application? How to reduce the database cost while ensuring the real-timeness? How to implement the cross-database query easily? How to reduce the management cost and difficulty? This is the one of hottest topics being discussed among the CIOs or CTOs.

esProc is a good remedy to this stubborn headache. It is the database middleware with the complete computational capability, offering  the support for the computing no matter in external storage, across databases, or parallel. The combination of database and esProc can deliver enough capability to solve the four challenges to big data applications.

esProc supports for the computation over files from external storage and the HDFS. This is to say, you can store a great volume of historical data in several cheap hard disks of average PCs, and leave them to esProc to handle. By comparison, database alone can only store and manage the current core business data. The goal of cutting cost and diverting computational load is thus achieved.

esProc supports the parallel computing, so that the computational pressure can be averted to several cheap node machines when there are heavy workload and a great many of parallel and sudden access requests. Its real-timeness is equal or even superior to that of the high-end database.

esProc offers the complete computational capability especially for the complex data computing. Even it alone can handle those applications involving the complex business logics. What's even better, esProc can do a better job when working with the database. It supports the computations over data from multiple data sources, including various structural data, non-structural data, database data, local files, the big data files in the HDFS, and the distributed databases. esProc can provide a unified JDBC interface to the application at upper level. Thus the coupling difficulty between big data and traditional databases is reduced, the limitation on the single-source report removed, and the difficulty of the big data application reduced.

With the seamless support for the combined computation over files stored in external storage and the database data, users no longer need the complex and expensive data synchronization technology. The database only focus on the current data and core business applications, while esProc enable users to access both the historic data in external storage and the current business data in database. By doing so, the latest and promptest analysis result can be delivered on time.

The cross-database computation and external storage computation capability of esProc can ensure the real-time query while alleviating the pressure on database. Under the assistance of esProc, the big data real-time application can be implemented efficiently at relatively low cost.

More Stories By Jessica Qiu

Jessica Qiu is the editor of Raqsoft. She provides press releases for data computation and data analytics.

CloudEXPO Stories
Eric Taylor, a former hacker, reveals what he's learned about cybersecurity. Taylor's life as a hacker began when he was just 12 years old and playing video games at home. Russian hackers are notorious for their hacking skills, but one American says he hacked a Russian cyber gang at just 15 years old. The government eventually caught up with Taylor and he pleaded guilty to posting the personal information on the internet, among other charges. Eric Taylor, who went by the nickname Cosmo the God, also posted personal information of celebrities and government officials, including Michelle Obama, former CIA director John Brennan, Kim Kardashian and Tiger Woods. Taylor recently became an advisor to cybersecurity start-up Path which helps companies make sure their websites are properly loading around the globe.
ClaySys Technologies is one of the leading application platform products in the ‘No-code' or ‘Metadata Driven' software business application development space. The company was founded to create a modern technology platform that addressed the core pain points related to the traditional software application development architecture. The founding team of ClaySys Technologies come from a legacy of creating and developing line of business software applications for large enterprise clients around the world.
To Really Work for Enterprises, MultiCloud Adoption Requires Far Better and Inclusive Cloud Monitoring and Cost Management … But How? Overwhelmingly, even as enterprises have adopted cloud computing and are expanding to multi-cloud computing, IT leaders remain concerned about how to monitor, manage and control costs across hybrid and multi-cloud deployments. It’s clear that traditional IT monitoring and management approaches, designed after all for on-premises data centers, are falling short in this new hybrid and dynamic environment.
Most modern computer languages embed a lot of metadata in their application. We show how this goldmine of data from a runtime environment like production or staging can be used to increase profits. Adi conceptualized the Crosscode platform after spending over 25 years working for large enterprise companies like HP, Cisco, IBM, UHG and personally experiencing the challenges that prevent companies from quickly making changes to their technology, due to the complexity of their enterprise. An accomplished expert in Enterprise Architecture, Adi has also served as CxO advisor to numerous Fortune executives.
DevOpsSUMMIT at CloudEXPO, to be held June 25-26, 2019 at the Santa Clara Convention Center in Santa Clara, CA – announces that its Call for Papers is open. Born out of proven success in agile development, cloud computing, and process automation, DevOps is a macro trend you cannot afford to miss. From showcase success stories from early adopters and web-scale businesses, DevOps is expanding to organizations of all sizes, including the world's largest enterprises – and delivering real results. Among the proven benefits, DevOps is correlated with 20% faster time-to-market, 22% improvement in quality, and 18% reduction in dev and ops costs, according to research firm Vanson-Bourne. It is changing the way IT works, how businesses interact with customers, and how organizations are buying, building, and delivering software.