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The New Big Data World GETTING MORE VALUE from DATA: the RISE of HIGH-PERFORMANCE DATABASES

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The New Big Data World GETTING MORE VALUE from DATA: the RISE of HIGH-PERFORMANCE DATABASES The New Big Data World GETTING MORE VALUE FROM DATA: THE RISE OF HIGH-PERFORMANCE DATABASES TECHNOLOGY WHITE PAPER MARCH 2019 Contents THE NEW BIG DATA WORLD – GETTING MORE VALUE FROM DATA: THE RISE OF HIGH-PERFORMANCE DATABASES Foreword - The database world has changed 1 1. TECTONIC SHIFTS IN THE DATABASE MARKET 2 Relational database vendors are challenged 2 Mainstream players have reacted with acquisitions 6 SAP pushes in-memory databases to primetime 8 2. NEW DATA MANAGEMENT TECHNOLOGIES 10 From SQL to NoSQL to NewSQL 10 Hadoop for big data: the rise and fall? 14 NoSQL databases: simpler than SQL 16 NewSQL databases 20 The database market has seen seismic change in the last decade. Cloud data warehousing 22 Trends including cloud computing, Big Data and IOT, AI and automation 3. CLOUD-BASED DATA WAREHOUSING: THE SNOWFLAKE CASE 24 have contributed to an explosion in data volumes. This has shaken up Snowflake, a ‘superfunded’ company 24 the typical enterprise data management ecosystem and created fertile ground for a new generation of startups offering high-volume, Snowflake’s approach: 100% ‘as-a-service’ 26 high-performance applications. Many mainstream relational database 4. IN-MEMORY ANALYTICS DATABASE: THE EXASOL CASE 28 players have reacted by acquiring the newcomers. Other big players Exasol, a hidden champion 28 such as SAP have championed in-memory databases. 5. CONCLUSION 30 Relational databases still dominate globally. But while the segment is expected to keep growing, driven by factors such as in-memory SQL databases, its global share is forecast to fall steadily. At the other end of the market, NoSQL and Hadoop databases surged by an estimated 66% per year from 2013-18 and are expected to account for 18% of the total database market by 2021. The excitement around new database technologies has been reflected in a wave of pre- IPO funding rounds dominated by Cloudera and Snowflake. However, cloud innovations and plummeting storage costs have driven consolidation in the Hadoop space. NoSQL databases, which offer simplicity and speed in big data applications, and NewSQL, which adds the ACID guarantees of relational databases, have seen significant activity and interest. To examine some aspect of the evolving database market in more detail, we profile cloud-based data warehousing software vendor Snowflake, a ‘superfunded’ company offering data warehouse as-a-service; and we look at Exasol, which develops in-memory analytics for big data use cases. In conclusion, we see a continuing tussle between Oracle and Amazon Web Services for the #1 spot in the database market, Hadoop being challenged by its own complexity, acquisitions in new database technologies as incumbents continue to battle AWS, and plenty of potential unicorns in the data management space. 1 1. Tectonic shifts in the database market Relational database vendors the backbone of mainstream Over the last decade, the database FIG.FIG 2: 2 ANNUAL SIZE OF THE ‘GLOBAL DATASPHERE’ IN ZETTABYTES (2010-2025E) are challenged enterprise application software like market has undergone massive ERP or CRM that flourished in the changes, thanks to the nature of data Gartner estimated the size of the 175 ZB 1990s and 2000s. (SQL was first being generated, the volume, and 180 database management systems developed by IBM in 1974 and made the processing capability needed to market at USD 38.8bn for 2017, commercially available by Oracle make sense of all the data. According 160 up 12.7%1, after an 8.1% rise in five years later). As they constantly to an IDC white paper published in 2016 and a 1.6% decline in 2015. 140 improved in performance, driven by November 20182, the total volume of For more than two decades, this the data warehousing, data mining global data has exploded, rocketing 120 market has been dominated by three and business intelligence tools that to 33 zettabytes (ZB) in 2018 from players: Oracle, which had 41% 100 appeared throughout the 1980s less than 5 ZB in 2005. It is projected market share in 2016, IBM (20%), and 90s, relational databases were to reach 163 ZB in 2025. One Zetabytes 80 and Microsoft (15%). Relational fast enough to address all of an zettabyte is equivalent to a trillion databases based on Structured 60 organisation’s data processing needs. gigabytes (GB). Query Language (SQL) have become 40 20 FIG. 1: DATABASE MANAGEMENT SYSTEMS MARKET SHARES (2016) 0 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 FIG 1 Source: IDC and Seagate, The Digitisation of the World, November 2018 MICROSOFT IT departments of large and small inexpensive technologies such Google Cloud BigTable (wide column companies alike are scrambling as software-defined storage (SDN), store), Google Cloud Spanner 20% to bridge this data infrastructure they made data storage and the (NewSQL database), Google Cloud IBM gap as they failed to anticipate this general use of data analytics Datastore (NoSQL database) and data explosion, which was primarily accessible to organisations of all Google BigQuery (data warehouse); 15% caused by six converging trends: sizes and across all departments. 3) Microsoft launched Microsoft Azure These three players developed their SQL Database and Microsoft Azure 41% Cloud computing own data management systems: Data Warehouse. The public cloud services Amazon 1) Amazon launched Amazon RDS Big Data ORACLE Web Services (2002), Google Cloud (relational database), Amazon 24% Platform (2008) and Microsoft DynamoDB (NoSQL database), As we have already seen, data volumes Azure (2010) have been designed Amazon Aurora (relational database) have increased massively. Social media to facilitate agile projects and and Amazon Redshift (data platforms create plenty of it: the overall OTHER provide flexible, demand-oriented warehouse); 2) Google launched data footprint of eBay, Facebook (AMAON, TERADATA, SAP) infrastructure and services. Using Google Cloud SQL (SQL database), and Twitter now is 500, 300 and 300 1Gartner, State of the Operational DBMS market, 2018, 6th September 2018. 2IDC and Seagate, The Digitisation of the World, November 2018. 2 | THE NEW BIG DATA WORLD 3 petabytes, respectively (a petabyte such as cameras or vending machines, and include the most recent data, External data self-driving cars and predictive stored in a ‘data lake’, which can is equivalent to one million GB). And has created new challenges for data meaning that the patience of users analytics based on self-learning then be connected to enterprise Data is no longer created only within the Internet of Things (IoT) – data from storage and analysis. waiting for reports, analyses and algorithms are radically changing data warehouses. the organisation itself. It can now be phones, cameras, connected cars, results is increasingly short. business processes. easily enriched with all manner of wearables, sensors, operational control Self-service data analytics Another consequence of these external sources, for example: social centers – generates vast volumes of Operational business intelligence These trends have shaken up the changes is the advent of Amazon By design, access to legacy data media, public statistics, geospatial and automation typical enterprise data management Web Services (AWS) as the fourth data and needs new technologies warehouses was restricted to data, weather forecasts, competition ecosystem. It is no longer limited to largest database vendor in the market for analysis, more scalable storage, centralised IT departments. The Data analytics no longer simply analysis and event-driven data. structured data, relational databases, (with a Cloud database offering). streaming solutions and operational data decentralisation of data and the measures historical data. It is being ETL (Extract Transform Load) tools, In 2017, AWS had an estimated systems. New applications also need to advent of more easy-to-use analysis built into all kinds of operational Artificial intelligence enterprise data warehouses, and 9%+ market share of the whole be created on top of data, for example tools like Tableau, Qlik, or Microsoft processes and is predictive, With recent advances in infrastructure traditional business intelligence tools. database market and revenues up to offer dynamic pricing or personalised Power BI enabled staff to access and deterministic and self-learning. and tools, data science is much In particular, structured data, an impressive 112% in this area. marketing campaigns. The complexity analyse data and get more insights Completely automated and self- more accessible and now allows semi-structured data, unstructured of IoT, where data is processed either from it. User requirements have also learning processes make decisions for automated decision-making. data and machine-based data on servers, public or private clouds, or changed dramatically. Dashboards based on data at a speed and Applications such as face recognition, (IoT, logs, sensors) can be now at the endpoint or the edge of devices can be updated within seconds accuracy that humans cannot match. FIG. 3: TYPICAL MODERN DATA MANAGEMENT ECOSYSTEM STRUCTURED DATA SEMI-STRUCTURED DATA UNSTRUCTURED DATA MACHINE-GENERATED DATA RELATIONAL DATABASES NON- (SQL, NEWSQL) RELATIONAL DATABASES (NOSQL) ETL TOOLS (EXTRACT TRANSFORM LOAD) DATA LAKE DATA WAREHOUSE BUSINESS ANALYTICS AND BUSINESS INTELLIGENCE TOOLS 4 | THE NEW BIG DATA WORLD 5 Mainstream players have in-memory databases, but these were These newly acquired technologies reacted with acquisitions confined to high-end applications. contained their own weaknesses. For example, they are all built on a Relational database management In the mid-2000s, start-ups including kernel of the PostgreSQL database software leaders have had to Vertica, Greenplum, Netezza, (a pre-existing database based on embrace these trends, but at the DatAllegro, Aster Data, ParAccel Ingres) which was built in the 1970s, same time deal with hundreds of and Exasol acknowledged that revamped in the 1990s, and difficult thousands of customers who need greater performance was going to to change. The legacy players backwards compatibility with legacy be needed to deal with increasing attempted to lock in the customers IT environments.
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