Strategic Briefing Paper Big Data
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- Caitlin Hutchinson
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1 Strategic Briefing Paper Big Data The promise of Big Data is improved competitiveness, reduced cost and minimized risk by taking better decisions. This requires affordable solution architectures which are able to react in real time and keep processing time constant while volumes are increasing. Fujitsu provides optimized Big Data infrastructure solutions based on a blend of various technologies, including hardware, software and services. Contents Some dreams of today s organizations 2 Business Intelligence for data-based decision support 2 Today s demands are different 2 What Big Data is about 2 Why traditional infrastructure approaches don t work 2 Distributed Parallel Processing 3 In-Memory Databases (IMDB) 3 Disk array with In-Memory Data Grid (IMDG) 3 NoSQL databases 4 Complex Event Processing (CEP) 4 Big Data solution architecture 5 Big Data is not just about infrastructure 6 How Fujitsu can help 6 Summary 6 Page 1 of 6
2 Some dreams of today s organizations Let us start with some dreams that today s organizations might dream when thinking about ways to improve their business: What if you could predict what will happen in terms of trends, customer behavior, or business opportunities? What if you could always take the best decisions? What if you could accelerate your decisions? What if you could have critical actions initiated automatically? What if you could fully understand the root cause of issues or costs? What if you could skip useless activities? What if you could quantify and minimize risks? What Big Data is about Big Data is characterized by large data volumes, of a variety of data types, coming from versatile data sources, which are generated at a high velocity and need to be analyzed at the same high velocity too, in order to create a high value for the organization and make the dreams we started with come true. Contemplating such questions, many managers immediately imagine the opportunities for their business. However, are these only nice dreams, or is there a chance that these dreams can come true? Business Intelligence for data-based decision support Remember the days when Business Intelligence emerged. People had recognized that utilizing data in an intelligent way could support decisions based on real facts rather than intuition, thus helping improve operations, minimize risk, increase revenue and gain competitive advantage. However, people also realized that data in its original form was usually of low value. Therefore structured data in the Gigabytes to Terabytes range was collected from few internal data sources, mainly relational databases, and then consolidated and transformed into a form convenient for analysis in order to discover relations, patterns and principles, to finally find the real value. Usually people analyzed periodically what had happened in the past. And it did not matter whether such an analytics job took hours or days. Today s demands are different Meanwhile the situation has changed tremendously. It is clearly understood that considering structured data from just a few relational databases means that most of the valuable data out there would remain untapped. Versatile internal and external data sources such as databases, text, audio and video files, IT logs, web logs, social media and the millions and billions of sensors which are built in everywhere can provide an enormous value, too. We are talking about large but ever increasing volumes of in the range of many Terabytes to Petabytes. Especially new sources provide data which rarely is structured, but rather unstructured, semi-structured and poly-structured. Very often this data is continuously generated at a very high velocity, and the same high velocity is also demanded when it comes to analyzing the data in real-time to create value for the organization. Getting the right insight and the desired analytics results in real-time opens up numerous new business opportunities for every organization, no matter to which industry it belongs. Why traditional infrastructure approaches don t work One of the biggest challenges with Big Data is to keep processing time constant while data volumes increase. This in turn has a strong impact on the infrastructure for Big Data. Big Data includes data at rest and data in motion. In a first step we will concentrate on data at rest. In traditional Business Intelligence solutions, the consolidated data for analytics is stored in a retrieval-optimized relational database, denoted as data warehouse. Relational databases are based on a rigid schema, which means that unstructured and semi-structured data would require some pre-processing. The tables resulting from pre-processing are often huge but sparse. This in turn means a high amount of metadata, high storage capacities and slow access to data. Furthermore, in a relational database, data is stored in rows. This is well-suited for Online Transaction Processing (OLTP), but when it comes to OLAP (Online Analytical Processing), much irrelevant data has to be read. This is because only certain information in certain columns is relevant for the queries. Why not use a powerful server and scale it up when data size increases? No matter how powerful your server might be, there will always be a hard limit for each resource type and therefore for the overall performance of the server. For sure, in the future the limits will move upwards, but the totality of all data involved in your analysis might grow much faster. Doing the right things right by predicting ad-hoc what is going to happen in the future is the weapon for organizations in today s competitive markets. This is exactly what Big Data is about. If scale up is not an option, the question remains about relational databases and server scale out. Sharing the database among several servers, the storage connections might prove to be a bottleneck when accessing the database. Likewise, the effort to coordinate the access to shared data will increase with the number of database servers. This will lead to decreasing server efficiency and limit parallelization. Page 2 of 6
3 Consequently, everything you try to improve the situation, be it scale up or scale out in conjunction with your relational database, would be time-consuming and expensive and far away from fulfilling real-time demands. Results would be returned too late and insights could be already obsolete when being presented to the user. For sure, you could think about building distinct databases and split up your analytics tasks. However doing so, you would create disconnected data silos and conduct distinct disconnected analytics which would not give you the comprehensive insight that you expect. Due to all limitations mentioned, new approaches are required, which can really keep processing time constant, while data volumes increase. Distributed Parallel Processing The idea of Distributed Parallel Processing is to distribute the data and thus the I/O load across the local disks of a server cluster and move the computing tasks exactly to the server nodes where the data resides. It is beyond all questions that the access to data on disk storage systems or even AFA (All-Flash-Arrays) providing highest I/O performance and low latency can never be as fast as if data are resident in main memory, and hence closer to the applications. That s why for real-time demands the distilled essence of the transformed data is consolidated into a fast responding in-memory platform. In-Memory Databases (IMDB) An In-Memory Database (IMDB) is characterized by loading its data entirely along with the database applications into the main memory of a server or server cluster to conduct rapid analysis. For analytics tasks, I/O is totally eliminated, because no disks are involved. Disks are just needed for snapshots of the resident data and change logs, to counteract the possible loss of memory content, e.g. after power outage. Fast recovery from server failure can be also achieved by mirroring the IMDB. A relational IMDB is the solution of choice, if several independent applications access the data from different viewpoints. The main limiting factor of an IMDB is the data size, because data size is limited by the overall memory capacity in the entire server cluster. Everything can then happen in parallel, nothing is shared; and therefore scalability is basically unlimited. You may start small, and whenever capacity or performance is not sufficient any more, you add more servers. Fault-tolerance can be achieved by replicating the same data to several nodes. Using cheap standard servers with Hadoop Open Source Software (Distributed File System, MapReduce and more) makes distributed parallel processing affordable. Distributed Parallel Processing is a great method to deal with large amounts of data fast and efficiently. But it does not support analysis in real-time. Therefore, in many cases, Distributed Parallel Processing is just used to transform data into a shape which is more appropriate for ad-hoc queries. The result of this transformation is typically much smaller than the initial data volume, but it contains all essential information for the respective use case. Thus, it may be seen as the distilled essence of the overall data. Disk array with In-Memory Data Grid (IMDG) If you organize the distilled essence of your transformed data on disk storage, an IMDG (In-Memory Data Grid) between disk storage and applications is worth considering. An IMDG is a memory-resident cache which can be distributed across several servers. By reducing the I/O load, it can be avoided that I/O becomes the bottleneck when a real-time analysis is intended. An IMDG can be applied, even if the size of the transformed data on disk exceeds the overall available main memory. Moreover, an IMDG can manage any data object which typically fits to the application semantics much better than a relational table. Combined with indexing and search, the IMDG becomes an in-memory object store, which has efficiency and performance advantages compared to IMDB. Data replication to multiple servers ensures data consistency and quick failover after a server crash. Moreover, using solid state disks in the individual servers as a persistence layer prevent data loss after power outage. Page 3 of 6
4 An IMDG is a great choice, in particular, if its data is not subject to a high update frequency, if the applications accessing the data can be used as they are, or if the skills are available to adapt the application in order to fully exploit the potential benefits of an IMDG. Of course it is also imaginable that Distributed Parallel Processing is used to transform the data, but the transformed data remain in the distributed Big Data store. An IMDG between the analytics applications and the distributed Big Data store will reduce I/O, accelerate analytics and can enable results of queries in real-time, too. Both concepts, IMDB and IMDG are able to scale-up and scale-out. Complex Event Processing (CEP) While the focus of the previous sections was on data at rest, we are now going to have a closer look at data in motion. Event streams are generated continuously at a very high velocity, for instance by sensors. These event streams have to be collected and analyzed on the fly in real-time. Depending on their relevance, events are filtered and then correlated. The analysis is based on a set of pre-defined rules that include a condition and an action. If the condition (which may be a correlation of logical and timely aspects) is fulfilled, the action will be triggered in real-time. This is how a CEP engine works. NoSQL databases Relational databases are best suited for structured data of a limited size. Big Data exceeds the size limits and includes lots of data which are not structured. NoSQL (Not only SQL) databases provide the advantages of database systems, for instance the easy query options, without the limitations of relational databases. They are based on a flexible data model, are designed to be distributed and for scale-out. There are various implementations of NoSQL databases for different use cases. The best-known examples are key value stores, document stores, graph databases and column-oriented databases. In a column-oriented database, data is stored in columns rather than rows. Thus, for queries only the columns are of interest have to be read. This reduces I/O and accelerates analytics. As a certain column contains only one type of data, and quite often there are only few distinct values per column, it is possible to store only these few distinct values and references to these values. Doing so, you achieve an extremely high compression. Typical compression factors are in the range from 10 to 50. This helps reduce storage capacities and consequently storage costs, too. In order to avoid time-consuming I/O, the collecting tank for the event streams is organized in-memory. In-memory data grids (IMDG) help meet higher main memory capacity demands. Depending on the intensity of the event streams, and/or the multitude of rules to be processed, it can be helpful to distribute CEP engines across several servers to enable parallel processing. The same applies for complex queries which cannot be executed by a single server. In this case, the complex queries will be split and distributed, too. Page 4 of 6
5 Big Data solution architecture With all the individual concepts, we are now in a position to present the overall Big Data solution architecture. Let us just give a brief summary of what is shown in the figure below. Data is extracted from versatile data sources. In addition to the mainly structured data from transactional databases, Data Warehouses and in-memory databases (IMDB), unstructured, semi-structured and poly-structured data is extracted and collected from versatile other sources, such as text, audio and video files, IT logs, web logs and sensors. Data at rest is loaded into the Distributed Data Store. Distributed Parallel Processing based on the MapReduce algorithm is used to transform data into a usable form and for analysis. The distilled essence of the transformed data can be exported to a SQL or NoSQL database management system. If a traditional disk storage array does not provide the required I/O performance when accessing the SQL database, a fast storage system, as for instance an AFA can be used to increase throughput and reduce latency, thus accelerating I/O. Real-time analysis is enabled by in-memory data platforms. An IMDB will be the fastest option, especially if new queries are created again and again. If the size of the distilled essence exceeds the totally available main memory capacity, a disk array with an IMDG in front can be used to accelerate the analytics applications, especially if data generated once is often read. Another advantage of IMDG is that is applicable to any data objects. Of course, an IMDG will also help reduce I/O, if the analysis is directly applied to the Distributed Data store. Event streams generated at a high velocity are collected in the data store of a CEP (Complex Event Processing) engine which in turn will look after analyzing the event streams and initiating the respective actions in real-time. As CEP is extremely time-critical, the data store is memory-resident, and often implemented as an IMDG. Depending on the use case, results of CEP can be forwarded into the Distributed Data Store or the distilled essence for further processing, in the same way as data from the Distributed Data Store is sometimes used for CEP. Page 5 of 6
6 Big Data is not just about infrastructure Infrastructure is a very important aspect of Big Data. However, it is not just about infrastructure, it is also about data and processes. This includes a lot of questions which need to be answered: Which data from which data sources to involve? How to transform data into high quality information? What to look for? Which questions to ask? What to do with the answers? Which analytic methods? How to visualize results? Which tools to use, and how to use them? What about data retention and deletion? To find the right answers, analytic skills and a deep knowledge of data, tools and intended results are required. How Fujitsu can help Regarding Big Data, Fujitsu covers the aspects of data, processes and infrastructure. In an end-to-end service approach, we consult our customers in terms of what can be achieved by Big Data and jointly define the roadmap to Big Data. We schedule and prioritize the use cases, design and implement the solution, we integrate it into the existing IT landscape, and we maintain the overall solution. What is more, Fujitsu stands for flexibility and choice in terms of sourcing options. If a customer wants to be relieved from routine tasks in order to focus his resources on other strategic projects, we will take over the operation. This is exactly what Fujitsu Managed Infrastructure Services are about. In addition, Big Data services are also delivered from the Fujitsu Cloud. Integrated Systems lower the entry barrier and enable a fast time-to-value. Especially worth mentioning are FUJITSU Integrated System PRIMEFLEX for Hadoop (a Hadoop cluster enabling even business users to conduct analytics in an easy manner) and FUJITSU Integrated System PRIMEFLEX for SAP HANA (SAP s IMDB solution). Attractive financing options enable the introduction of Big Data solutions without any pre-investment. Summary Big Data is more than large data sets. It is about large volumes of various types from versatile sources generated at a high velocity. The objective of analyzing Big Data in real-time is to predict things that are going to happen in future, to take the right decisions, and to initiate the respective actions, which will help to improve operations, reduce costs, minimize risks and create value for the organization, i.e. to gain real competitive advantage. The challenge regarding Big Data infrastructures is to keep processing time constant while data volumes increase and keeping it affordable at the same time. Therefore new approaches, such as Distributed Parallel Processing, IMDB, IMDG, AFA and CEP are needed. Fujitsu is a one-stop shop for Big Data where organizations can get optimized infrastructure solutions based on a blend of various technologies, including hardware, software and services all from a single source. Fujitsu supports all relevant Big Data infrastructure concepts, and will thus always elaborate the optimum combination of technologies according to the customer-specific situation and requirements. The well-designed solution stack includes proven servers and storage systems from Fujitsu. Software is either from Fujitsu itself, open source, or from leading independent software vendors with whom we have a close partnership. Contact FUJITSU Technology Solutions GmbH Address: Mies-van-der-Rohe-Strasse 8, Munich, Germany Website: WW EN ƒ Copyright 2016 Fujitsu, the Fujitsu logo are trademarks or registered trademarks of Fujitsu Limited in Japan and other countries. Other company, product and service names may be trademarks or registered trademarks of their respective owners. Technical data subject to modification and delivery subject to availability. Any liability that the data and illustrations are complete, actual or correct is excluded. Designations may be trademarks and/or copyrights of the respective manufacturer, the use of which by third parties for their own purposes may infringe the rights of such owner. Page 6 of 6
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