The active players and the competitive field

Players in the competitive field can influence the future development of RIMDB in different directions and at different points. Based on online research and the results from the expert interviews in R1, the active players relevant to the field of RIMDB can be split into six groups as described below. Please note that this is not an exhaustive list naming all players explicitly. The names of players that are cited should only serve as examples for describing the player groups and facilitate better understanding. (1) Research institutes

Research institutes investigate the topic of RIMDB, as well as other topics related to big data analysis. For Berlin and its surrounding area alone, there are the examples of the Hasso-Plattner-Institut (HPI) (who e.g. offers an open online course9), the Database Systems and Information Management Group (DIMA) at Technical University of Berlin10, and the Berlin Big Data Center11. Worldwide, there are many more examples, such as the Massachusetts Institute of Technology (MIT) Database Group12 to just name one. Research in RIMDB and other big data technologies can impact the future development of RIMDB considerably, amongst others by finding solutions for the issues identified above.

(2) Governments and regulatory entities

Government organizations are relevant to the field of RIMDB when it comes to data privacy and data protection. Restrictive standards may influence RIMDB, for example regarding the use of cloud technology. For Europe, the European Commission (EC) plays a relevant role. The EC recognizes both

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For more information, see https://open.hpi.de/courses/imdb2015 10

For more information, see https://www.dima.tu-berlin.de/ 11

For more information, see http://www.bbdc.berlin/start 12 _{For more information, see http://db.csail.mit.edu}

the opportunities big data could open up for the European Union (EU), as well as the potential problems with data protection that might occur (EC, 2015a; EC, 2015b). In January 2012, a reform of the rules for data protection was issued by the EC (EC, n.d.). There are also relevant national authorities, such as Die Bundesbeauftragte für den Datenschutz und die Informationsfreiheit (n.d.) in Germany, and regulatory guidelines, such as the Electronic Communications Privacy Act by the United States Congress (Doyle, 2012). Recently the international agreement Safe Harbour was declared invalid, intending to make it more difficult to send EU citizen data to the USA (Gibbs, 2015).

Additionally, governments and regulatory entities can invest into specific aspects of big data analysis, e.g. by financially supporting research and infrastructural measures, further highlighting their relevance for RIMDB.

(3) RIMDB vendors

There are a number of commercial, proprietary RIMDB products from large software vendors, for example Oracle TimesTen (Oracle Corporation, n.d; Oracle Corporation, 2015), SAP HANA (SAP America, Inc., n.d., a), IBM DB2 with BLU (IBM Corporation, n.d.) and Microsoft Hekaton (Diaconu et al., 2013). Unfortunately, no current data for market shares of RIMDB could be found. The Google Trends screenshot in Figure 6 below indicates search volumes for the named products. Because RIMDB are business tools mainly used by trained experts, and these experts may not need a Google search to research the products, the search volumes displayed by Google Trends cannot serve as a market share proxy. Nevertheless, Figure 6 can at least indicate efforts for publicity, with which SAP HANA leads the race since about 2012. The interviewed experts supported this notion.

Figure 6: Google Trends for “SAP HANA” (blue), “TimesTen” (red), “Hekaton” (yellow) and “IBM DB2” (green); Screenshot taken on 08.03.2016

In addition, MonetDBLite is an open-source DB using IM technology with relational characteristics, frequently named by the experts. Other RIMDB exist, although mainly commercially distributed ones. Moreover, some DB exist that are relational, but use both IM technology and disks in combination. Examples for the latter are HP Vertica13 and HyperSQL14. For the purpose of this thesis, we include these DB in the group of RIMDB.

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For more information, see http://www8.hp.com/us/en/software-solutions/advanced-sql-big-data- analytics/index.html?jumpid=va_udr5yqixwz

(4) Alternative and complementary technologies for big data analysis

There are a number of alternative as well as complementary technologies to RIMDB within the realm of big data analysis. As mentioned, there are non-relational/NoSQL DB, and DB that do not use IM technology, but are disk-based. That means alternative DB can (a) be using IM technology, but be non- relational (e.g. Hazelcast15, Redis16), or (b) be disk-based and either relational or non-relational. As explained, it may depend strongly on the type of data and the context which type of DB is used. Moreover, depending on both the application field in question and the strategic positioning, technologies can be either competitive or complementary to RIMDB: There is Hadoop®, a powerful open-source data processing framework managed by Apache™ which can essentially be turned into many things17. One Hadoop® project is Apache Spark™, a “(…) fast and general engine for large-scale data processing.” (The Apache Software Foundation, n.d.) which can combine various data analytics libraries. Another project is Apache Hive™ which is a data warehouse with distributed storage using its own language HiveQL (similar to SQL)18. There is also MapReduce, a system to process large datasets in parallel (Dean & Ghemawat, 2008). Many other Hadoop projects exist and are usually developed by different groups or companies (Hallenbeck, 2015). These projects can be competitors to RIMDB products for big data analysis, but Hadoop can also be used as a complementary asset to RIMDB. For example, SAP allows for use of their HANA technology together with the Hadoop framework and its solutions for additional processing power and use of e.g. NoSQL options in addition to their RIMDB (SAP America, Inc., n.d., b). Other examples for technologies that can be both competitive and complementary to RIMDBS are Amazon Web Services (AWS), a platform offering various big data storage, analytics and application solutions, and cooperate e.g. with RIMDBS vendors SAP HANA and Oracle regarding AWS cloud services (Amazon Web Services, Inc, n.d.). Also, Google offers its own cloud-based relational DB and big data analytics platform BigQuery which is compatible with other offers such as Hadoop and Spark (amongst other partners) (Google, Inc., 2015a; Google, Inc., 2015b). These alternative/competitive or complementary technologies and offers can influence the future development of RIMDB, but are also subject to similar influences as RIMDB (e.g. when it comes to regulatory restrictions for data privacy, to name just one).

(5) Potential customers of RIMDB

Larger businesses, as well as governments and any other institutes or legal entities with a need to store, process and analyse large amounts of data could be a customer for RIMDB, as described. Costs, high energy consumption and little need to process large amounts of data currently exclude many SMEs from the group of customers. This may change in the future, e.g. due to the expected cost decline. Potential customers of RIMDB are of very high relevance for the technology’s future

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For more information, see https://hazelcast.com/ 16

For more information, see http://redis.io 17

Find their list of projects here: http://hadoop.apache.org/ 18 _{For more information, see http://hive.apache.org/}

development, because the willingness of customers to invest into and implement a technology (or a competing technology) could shape the market development significantly.

(6) Third-party services

There are also third party services relating to RIMDB and their distribution. Firstly, consultancies (or freelance consultants) can offer guidance when customers want to choose a DB system, and help with implementation projects. Consultancies can also provide further information and research of their own on RIMDB. Secondly, third parties can supply data either directly to the RIMDB vendor who offers the data as a package-deal to its customers, or to the RIMDB customer as an additional service. Thirdly, RIMDB rely on network and connectivity to send and receive data. For example, it is possible that a business implementing a RIMDB has branches all over the world and wants to use the technology to collect and analyse data from all these outposts in real-time – which cannot be done without sufficient connectivity. Fourthly, there are providers of applications and additional services that cooperate with RIMDB vendors. One example for the latter are the members of SAP’s Startup- Focus initiative, through which SAP cooperates with start-ups offering solutions relating to big data, predictive or real-time analysis19.