Big Data Use Case: Business Analytics

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1 Big Data Use Case: Business Analytics Starting point A telecommunications company wants to allude to the topic of Big Data. The established Big Data working group has access to the data stock of the enterprise and is supported by experienced analysis experts. By being able to consolidate the mass of data available to the company in one single database for the first time and evaluate it for new insights, the enterprise expects to gain new business intelligence operating figures. Furthermore, the Big Data initiative is planned to evaluate existing internal processes for possibilities to increases in efficiency with the new technology. In addition, finding new business processes and providing added value to the customers in order to tie them to the company, thanks to the new possibilities, are both planned. Challenge In the working group, there is no know-how regarding the technical realization of a Big Data infrastructure and the creation of analysis algorithms for the different Big Data frameworks. The new system is designed to enable the work group to generate new ideas, evaluate them and in case of success bring them into productive use. The solution must fulfil very high security needs, because among other things, very sensitive (customer- / company-) data is constantly being evaluated. This is precisely why the telecommunications company is running their own private cloud infrastructure, which is based on OpenStack. Due to the size of the enterprise and the amount of ideas, several Big Data clusters need to operate in parallel, allowing different analysts to work simultaneously on their research tasks. For this, it is necessary to distribute the resources of the owned cloud among the teams in relevance to practical application.

2 Solution Because it perfectly fulfils the requirements, the telecommunications company decides to use Lambda- Now Enterprise: Thanks to the possibility to assemble different Big Data processing chains via a few clicks and the ability to deploy them to the company-owned cloud environment, the analysts don t need help from system administrators to test fresh technical approaches. With this in mind, they are able to instantly start working after minutes of deployment of the processing chain in the cloud. By including SQL translators for the used Big Data technologies, the lengthy learning process of a new input language is not necessary for the business analysts. In the Big Data world, the Lambda Architecture has established itself as a data management concept that is excellent for long term as well as real-time analyses. The Lambda Architecture is based on the idea of different layers for individual functional blocks, with each one horizontally scalable in order to adapt to the particular data flow demands. The great scalability of LambdaNow makes the extension of a cluster with some additional virtual machines very easy. Therefore, it is possible to raise the performance of a cluster from the needs of testing operations to the requirements of productive use with just a few clicks with the mouse. The employment of LambdaNow on the OpenStack cloud located in the company data center, ensures that the telecommunications company is always able to exercise full control over the data and the accesses of the different users. Thus, all legal requirements for the responsible handling of sensitive customer data are fulfilled. LambdaNow enables the Big Data administrator to assign an individual contingent of cloud resources, which he/she is allowed to use for every user of the platform. Informative widgets show the type and amount of the current occupied as well as available cloud resources. This enables both the administrator and the user to employ the processing power that is available in the company in a responsible way. On request, the frontend of LambdaNow Enterprise can be adapted to the corporate design of the company. The analyst then gets the feeling of using product of the own enterprise, which further increases the acceptance of the Big Data platform. In addition to that, with LambdaNow Enterprise in corporate design, the telecommunication company is capable of offering their own clients the possibility of using an easy and uncomplicated Big Data infrastructure, in turn developing new business areas without facing big development costs. By using the LambdaNow platform, the company was able to evaluate that the processing time for the daily analysis of the risk management data could be lowered from almost 24 hours down to just under four hours by employing a Hadoop based system. Phone: +49 (0) Mail: Web: App: IBAN: DE BIC: BEV0DEBB - Berliner Volksbank USt-ID: DE Managing Directors: Christian Richter Sebastian Kreusler AltusInsight GmbH Löwestrasse Berlin Register Berlin-Charlottenburg: HR B B

3 Big Data Use Case: Internet of Things Starting point An internationally operating equipment manufacturer (OEM) is selling cable modems to Internet Service Providers (ISP). The modems provide TV programs and Wi-Fi access to customers from the ISP. An average ISP operates to devices. The OEM wants to distinguish itself from its competitors by offering a premium service to its customers. The Wi-Fi communication is often disturbed, thus the support system provided by the ISP is stressed by concerned end users. In order to raise the service quality significantly, the Wi-Fi communication of the ISP s customers should be actively monitored and continuously analyzed to keep the transmission parameters at optimum levels. Challenge In order to faster detect Wi-Fi communication issues of the cable modem at an early stage and be able to react more quickly, the OEM wants to provide the ISPs with detailed real-time reporting. Each cable modem sends a log file of about 10 KB every ten minutes, generating 144 log files and approx. 1.4 MB of data a day. With cable modems per ISP, this means they are receiving log files and approx. 1.4 TB of data every single day. Conventional database systems surrender quickly when receiving and processing this level of data stream. The ISP hopes to substantially reduce the amount of support requests and, thereby, cut the costs by up to one million Euros per year.

4 Solution The lambda architecture has been established in the world of Big Data as a real-time system allowing long-term storage & analysis. It is based on the idea of different layers, each one being horizontally scalable in order to adopt to the particular data flow demands. The lambda architecture enables the OEM to perform real-time analysis of the current Wi-Fi status of all modems, as well as to analyze the complete parameter history of each device. The deployment and the operations of a complex lambda infrastructure are realized by the OEM in a public cloud environment. Due to the use of a SaaS solution, quick setup times are guaranteed and it is possible to react to increasing data streams instantly. With LambdaNow by AltusInsight, it is easy to reduce time to market, limit hardware expenses and precisely estimate service costs, while simultaneously running robust enterprise processes in a scalable environment. Technical background To import Wi-Fi log data into the cloud platform, an event-based REST API has been implemented. After importing, the data is forwarded to a Kafka cluster, responsible for persisting all data and offering downstreams to the analysis framework. The speed layer, based on Apach Samza, performs the real-time processing of the last 45 minutes of data. All computed results are presented to the support employees of the ISP in a real-time dashboard. The analysis of the long-term data takes place in the batch layer using an ETL process (extract, transform and load). The data is persisted on a long-term storage in a cloud environment (e.g. Amazon S3) and can be analyzed using Hadoop/Hive. The support team of the OEM has access to the Wi-Fi history of all devices and is subsequently able to perform analysis with the complete data set. Offering this service to an increasing number of ISPs is easy in both a public or OEM-owned private cloud. Phone: +49 (0) Mail: Web: App: IBAN: DE BIC: BEV0DEBB - Berliner Volksbank USt-ID: DE Managing Directors: Christian Richter Sebastian Kreusler AltusInsight GmbH Löwestrasse Berlin, Germany Register Berlin-Charlottenburg: HR B B

5 Big Data Use Case: Mobile Games Starting Point An international operating games publisher provides many different games at an online platform. The users of the platform are able to choose between different types of games (strategy, action, game of skill,...). All games offer the possibility to buy in-game digital goods to improve the gaming experience (e.g. weapons, skills for the avatar, etc.). Essential moves of the gamers are recorded with a tracking engine and analyzed in a data warehouse for each game separately. The evaluated data is used to optimize the game in regards to customer satisfaction, playability and generation of sales. Challenge In order to further increase the gamer s satisfaction and generate more turnover per game, a substantially greater amount of data shall be gathered. Due to the big success of the games, the previously existing system has reached its capacity limit. The employed relational database can t be horizontally scaled any further, thus making a change in technology necessary in order to increase the performance of the analyzing environment. The new analyzing environment should enable the data warehouse to collect and record multiple data points per gamer. In order to develop a better estimation of the gamer, the marketing department wants to evaluate the data of a gamer over different games.

6 Solution In the Big Data world, the Lambda Architecture has established itself as a data management concept that is excellent for long term storage and analyses. The Lambda Architecture is based on the idea of different layers for individual functional blocks, each one being horizontally scalable in order to adapt to the particular data flow demands. The new system is capable of collecting many more data points than before, thus delivering a more detailed view of the gamer s activities. The great scalability of the solution now allows the collection of data of all users in all offered games of the platform in one single data storage, without any difficulties. This makes new evaluation methods like the cohort analysis possible. Thanks to the improved quality of the data, the Business Intelligence department is now able to provide the Marketing with better information in regards to quantity as well as quality. The new data quality renders more informed decisions possible in terms of type and scale of marketing activities. Players that didn t make any progress in a gaming situation for any length of time can now be supplied with a specific promotion offer to keep them motivated, e.g. better weapons. Technical background The data entry is event-based and uses tracking servers, which collect the information of the different game servers. Afterwards, the data is forwarded to a Kafka cluster, which is responsible for persisting all data and offering down streams to the analysis framework. The data management is organized by an ETL-process (extract, transform and load). In the Lambda Architecture, the batch layer is responsible for storing the long-term data. In the present case, the batch layer is equipped with Hadoop YARN, accompanied by the Hadoop Distributed File System (HDFS) for data storage. The data is persisted in the long-term storage, afterwards it can be evaluated with Hive based on Hadoop Map/Reduce or Spark. At the beginning of the project, no real-time capabilities were implemented. Thanks to the very modular design of the Lambda Architecture, the analysis environment can easily be extended with a speed layer for real-time evaluation. This substantially increases the likelihood of buying digital goods and exhibits a sustained improvement in customer satisfaction. Phone: +49 (0) Mail: Web: App: IBAN: DE BIC: BEV0DEBB - Berliner Volksbank USt-ID: DE Managing Directors: Christian Richter Sebastian Kreusler AltusInsight GmbH Löwestrasse Berlin Register Berlin-Charlottenburg: HR B B

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