The New Big Data World - GETTING MORE VALUE FROM DATA: THE RISE OF HIGH-PERFORMANCE DATABASES - TECHNOLOGY WHITE PAPER MARCH 2019 - Bryan, Garnier ...
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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 FIG2:2 ANNUAL SIZE OF THE ‘GLOBAL DATASPHERE’ IN ZETTABYTES (2010-2025E)
FIG.
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, 160
commercially available by Oracle make sense of all the data. According
up 12.7%1, after an 8.1% rise in
five years later). As they constantly to an IDC white paper published in 140
2016 and a 1.6% decline in 2015.
improved in performance, driven by November 20182, the total volume of
For more than two decades, this 120
the data warehousing, data mining global data has exploded, rocketing
market has been dominated by three
and business intelligence tools that to 33 zettabytes (ZB) in 2018 from
Zetabytes
100
players: Oracle, which had 41%
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 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
20% companies alike are scrambling
to bridge this data infrastructure
as software-defined storage (SDN),
they made data storage and the
store), Google Cloud Spanner
(NewSQL database), Google Cloud
IBM gap as they failed to anticipate this general use of data analytics Datastore (NoSQL database) and
15% data explosion, which was primarily
caused by six converging trends:
accessible to organisations of all
sizes and across all departments.
Google BigQuery (data warehouse);
3) Microsoft launched Microsoft Azure
41%
These three players developed their SQL Database and Microsoft Azure
Cloud computing own data management systems: Data Warehouse.
The public cloud services Amazon 1) Amazon launched Amazon RDS
24%
ORACLE (relational database), Amazon Big Data
Web Services (2002), Google Cloud
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
(AMAZON, TERADATA, SAP…)
infrastructure and services. Using Google Cloud SQL (SQL database), and Twitter now is 500, 300 and 300
Gartner, State of the Operational DBMS market, 2018, 6th September 2018.
1
IDC and Seagate, The Digitisation of the World, November 2018.
2
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.
Self-service data analytics easily enriched with all manner of Another consequence of these
wearables, sensors, operational control
Operational business intelligence external sources, for example: social These trends have shaken up the changes is the advent of Amazon
centers – generates vast volumes of By design, access to legacy data
and automation media, public statistics, geospatial 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
systems. New applications also need to ETL (Extract Transform Load) tools, In 2017, AWS had an estimated
advent of more easy-to-use analysis built into all kinds of operational Artificial intelligence
be created on top of data, for example enterprise data warehouses, and 9%+ market share of the whole
tools like Tableau, Qlik, or Microsoft processes and is predictive,
to offer dynamic pricing or personalised With recent advances in infrastructure traditional business intelligence tools. database market and revenues up
Power BI enabled staff to access and deterministic and self-learning.
marketing campaigns. The complexity and tools, data science is much In particular, structured data, an impressive 112% in this area.
analyse data and get more insights Completely automated and self-
of IoT, where data is processed either more accessible and now allows semi-structured data, unstructured
from it. User requirements have also learning processes make decisions
on servers, public or private clouds, or for automated decision-making. data and machine-based data
changed dramatically. Dashboards based on data at a speed and
at the endpoint or the edge of devices Applications such as face recognition, (IoT, logs, sensors) can be now
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. This constraint made data volumes. They developed by offering ‘end-to-end’ business
it practically impossible to replace solutions in the form of large-volume, solutions (from databases to BI
30-40-year-old code and rebuild their high-performance data warehousing solutions) of which the relational
solutions from scratch. So instead applications designed and built for database was only one component.
of migrating their database product analytical use cases. Their products For example, for use in corporate
offerings to the new technology started to see success and most data centers deployed as private
platforms, these large incumbents of them were acquired by global IT clouds, Oracle launched in 2008 its
kept their old platforms and tried to players (see Fig. 4). ParAccel, which Exadata Database Machine, which
fix their legacy systems by adding has served as the core component is an Oracle database running on
elements of the new technologies of Amazon Redshift, was acquired dedicated hardware. Exadata was
to their existing products. The result by Actian (formerly Ingres) in 2013. adapted to public clouds in 2015.
has been a mix of approaches and These acquisitions enabled global
a hybrid architecture. Alternative vendors to temporarily address
solutions emerged, such as performance issues and consolidate
column-based data stores their market positions.
(like Sybase IQ in 1995) and
FIG. 4: ACQUISITIONS IN DATABASES AND DATA WAREHOUSING
Company Funding Price/acquirer
DatAllegro USD 63m USD 200m by Microsoft (2008)
Netezza USD 63m USD 1,700m by IBM (2010)
Greenplum USD 97m USD 300m by EMC (2010)
Vertica Systems USD 31m USD 350m by HP (2011)
Aster Data USD 53m USD 295m by Teradata (2011)
Source: Company Data; Bryan, Garnier & Co ests.
6 | THE NEW BIG DATA WORLD 7
SAP pushes in-memory FIG. 5: SAP HANA REVENUES (2011-2013) (EUR M) In response to the success of SAP governed data platform, with several data – including semi-structured and
databases to primetime FIG 5 HANA, which grew from nothing in product extensions and innovations for unstructured external data like clicks,
2010 to EUR 633m in revenues by orchestrating and managing enterprise logs, IoT, text, images, and video –
Before it launched HANA, SAP was
not a database player, even though 633 2013 and now reaches over 21,000
customers, relational database
business data within multi-cloud and
hybrid environments. To integrate Big
before it is surfaced through SAP HANA
for further, more detailed analysis.
in 1997 it had acquired Max DB, a
incumbents moved to embrace Data stored in Hadoop with enterprise
relational database with an in-memory As of today, the key components
in-memory technology and protect data in SAP HANA, SAP offers a
engine that was developed in the
late 1970s at the Technical University
392 their installed base. During 2013 and
2014, IBM, Oracle and Microsoft all
separate in-memory query engine, SAP
Vora, which was launched in 2015. And
of SAP HANA Data Management
Suite include SAP Data Hub -
of Berlin. For years, SAP relied on which creates a central metadata
launched in-memory options on top with the acquisition in 2016 of Big Data
IBM, Oracle and Microsoft to provide management catalog; SAP HANA;
of their relational databases, rather as-a-service (BDaaS) provider Altiscale,
the database that ran its enterprise
application software products. In
160 than releasing an independent SAP was able to create its SAP Cloud SAP Enterprise Architecture
Designer, which creates architecture
in-memory database. Platform Big Data Services offering
2010, the acquisition of Sybase for documentation; and SAP Cloud
based on Hadoop and Spark. This acts
USD 5.8bn gave SAP the Sybase ASE Over the years, SAP HANA has Platform Big Data Services.
as a data refinery, with the ability to
relational database (which served as 2011 2012 2013 evolved to become an open and cleanse and transform terabytes of raw
the basis for Microsoft SQL Server)
and the Sybase IQ column store. Source: SAP. From 2014 onwards, SAP stopped reporting SAP HANA revenues
The launch in November 2010 of the The HANA technology had several designed eliminate the use of ETL FIG. 6: ARCHITECTURE OF THE SAP HANA DATA MANAGEMENT SUITE
SAP HANA in-memory database, sold advantages from the start: 1) speed (Extract Transform Load) tools, stored
SAP INTELLIGENT ENTERPRISE SUITE SAP LEONARDO AND SAP ANALYTICS CLOUD THIRD-PARTY APPLICATIONS
as an application running on certified – the ability to handle several billion procedures, materialised views and
hardware, was a revolution in the records in seconds; 2) scalability OLAP cubes.
database world. SAP now mandated - designed to positively respond SAP HANA DATA MANAGEMENT SUITE
its own in-memory database for to the increase in the number of SAP HANA was made possible by SAP ADD-ON
API SERVICES
the arrival of standard servers at
HYBRID CLOUD MANAGEMENT
AND PRODUCTS
running its business applications, at cores per server; 3) in-memory data
affordable prices, equipped with
SAP CLOUD PLATFORM
the expense of third-party relational compression between 5 and 20x; and THIRD-PARTY
PRODUCTS
SAP HANA
SPATIAL
IN-MEMORY DATA DATA DATA DATA
database providers. The initial 4) data management simplification several terabytes of memory and AND SERVICES TRANSACTION DISCOVERY ORCHESTRATION CLEANSING STORAGE SERVICES
HANA
purpose of SAP HANA was to carry – with no need to create data multi-core processors. With blade SPARK
AND
ANALYTICS
AND
GOVERNANCE
AND
INTEGRATION
AND
ENRICHMENT
AND
COMPUTE BLOCKCHAIN
HADOOP SERVICES
out real-time data analytics and aggregates, there is no duplication. servers then sold for EUR 40,000 THIRD-PARTY HANA
from 2011, SAP used it to power a unit (now down to EUR 15,000), DATABASES STREAMING
THIRD-PARTY ANALYTICS
SAP Business Warehouse, its data In addition, HANA processed massively parallel computing DATA OTHER SAP
MANAGEMENT SAP ENTERPRISE CLOUD
warehousing system. But SAP HANA in-memory data in both lines and became widespread. In-memory data SAP HANA
ARCHITECTURE DESIGNER PLATFORM +
SAP LEONARDO
was quickly used to manage data columns, and could handle data processing technology has therefore SERVICES
SAP DATA HUB SAP CLOUD PLATFORM,
transactions, powering SAP Business management standards for long been reduced to the notion of a SAP EIM SOLUTIONS BIG DATA SERVICES
Suite (2013) then its replacement SAP relational databases and OLAP cache or database accelerator. While
S/4HANA (2015) and all SAP cloud (OnLine Analytical Processing) all operations take place in-memory,
applications. Today, SAP HANA is multi-dimensional analysis. Finally, each transaction is automatically BUSINESS
CLOUD
APPLICATION IOT SPATIAL SOCIAL IMAGE
the mandatory database for running with its ability to handle transactional captured and copied onto a disk with ON-PREMISES DATA
DATA MULTI-CLOUD
SAP’s Core and Cloud applications. and analytical data, SAP HANA was redundancy and high availability.
Source: SAP
8 | THE NEW BIG DATA WORLD 9
2. New data management
technologies
FIG 7
From SQL to NoSQL FIG. 7: DATABASE MANAGEMENT SYSTEMS MARKET (2013-2021E)
to NewSQL
Relational databases still account 60,000 DYNAMIC DBMS (NOSQL, HADOOP)
for around 82% of the global market LEGACY NON-RELATIONAL DBMS
50,000 RELATIONAL DBMS
for database management systems.
However, this has fallen from 87% 40,000
in 2013, and IDC anticipates that it
30,000
will fall further, to 77% by 2021. That
said, the relational database segment 20,000
is still expected to grow by 6-7% 10,000
per year over the next three years,
0
driven in particular by in-memory 2013 2014 2015 2016 2017 2018E 2019E 2020E 2021E
SQL databases or in-memory options
and the strong growth of NewSQL Source: IDC
databases. NoSQL, NewSQL will
not replace SQL databases, but
co-exist within technology stacks
for the foreseeable future. At the FIG. 8: PRE-IPO FUNDING OF NO/NEWSQL, HADOOP AND CLOUD EDW
FIG 8
other end of the market, NoSQL and VENDORS (USD M)
Hadoop databases have increased
their market share from 1% in 2013
to an estimated 11% in 2018, with 1,000
revenues up from USD 366m to 929
USD 4.6bn. These databases have
surged by an estimated 66% per
year on average over the period. IDC
anticipates that this segment will see
average annual growth of 33% from
311 280
2017-2021, reaching USD 10bn or 248 247
18% of the total database market
190 173 162 160 146
108
by 2021.
CLOUDERA
SNOWFLAKE
MONGODB
MAPR
HORTONWORKS
DATABRICKS
DATASTAX
MARKLOGIC
ELASTIC
COUCHBASE
NEO4J
MEMSQL
The excitement around NoSQL,
NewSQL and Hadoop, which
are aimed at replacing relational
databases, is reflected by the funding
of a significant number of startups in Source: Crunchbase
these areas (see Fig. 8).
10 | THE NEW BIG DATA WORLD 11
The table below recaps the financing Yellowbrick Data (USD 92m), Neo4j and Elastic – the four companies FIG. 10: MARKET CAPITALISATION OF NEW DATA MANAGEMENT TECH VENDORS
rounds that have taken place since (USD 80m), NuoDB (USD 31m) and in this space that have gone public
early 2016 in the database and data MemSQL (USD 30m). In 2017, the since 2014 – show strong investor Elastic USD 5.19bn +107% since IPO
warehousing areas. 2018 saw plenty two biggest rounds were Databricks appetite for these stocks as they Mongo DB USD 4.59bn +181% since IPO
of activity, with USD 713m raised (USD 140m), and Snowflake again generate strong growth, reduce
Cloudera USD 1.72bn -23% since IPO
for Snowflake Computing, which (USD 105m). their losses, and become potential
is seen as the new ‘unicorn’ in the champions in their specific area or Hortonworks USD 1.24bn* +88% since IPO
space, over two rounds. The other Valuation multiples achieved by M&A targets for the likes of IBM,
Vertica Systems USD 31m
significant rounds for this year include Hortonworks, Cloudera, MongoDB Oracle, SAP and others.
Aster Data USD 53m
FIG. 9: FINANCING ROUNDS FOR NO/NEW SQL, HADOOP AND CLOUD EDW VENDORS *As of 2 January 2019, when it left the stock market following merger with Cloudera.
Date Company Round No. investors Amount Lead financing
Dec. 2018 NuoDB Venture 5 USD 31m Temenos
Current EV/(N+1) sales multiples are impressive for Elastic and MongoDB:
Nov. 2018 Neo4j Series E 5 USD 80m Morgan Stanley, One Peak
Oct. 2018 Yellowbrick Data Series B 2 USD 48m Next47
FIG11:
FIG. 11 EV/(N+1) SALES MULTIPLES AS OF 8TH JANUARY 2019
Oct. 2018 Snowflake Computing Series F 8 USD 450m Sequoia
Oct. 2018 Elastic IPO USD 252m Valuation: USD 2.5bn
Aug. 2018 Yellowbrick Data Series A 5 USD 44m Menlo Ventures 19
May 2018 MemSQL Series D 5 USD 30m Glynn, GV
18.5
Jan. 2018 Snowflake Computing Series E 3 USD 263m Altimeter, Iconiq, Sequoia
Oct. 2017 MongoDB IPO USD 256m Valuation: USD 1.6bn
Sep. 2017 MariaDB Series C 6 USD 27m Alibaba
Sep. 2017 Snowflake Computing Series D 7 USD 105m Iconiq
Sep. 2017 MapR Technologies Venture 7 USD 56m Lightspeed
Aug. 2017 Databricks Series D 4 USD 140m Andreessen Horowitz
May 2017 MariaDB Venture 1 EUR 25m European Investment Bank
3.2
May 2017 Cockroach Labs Series B 5 USD 27m Redpoint
Apr. 2017 Cloudera IPO USD 225m Valuation: USD 1.9bn
Dec. 2016 Databricks Series C 3 USD 60m New Enterprise Associates ELASTIC MONGODB CLOUDERA
Nov. 2016 Neo4j Series D 4 USD 36m
Source: Company data; Thomson Reuters
Aug. 2016 MapR Technologies Venture 7 USD 50m Future Fund
Jul. 2016 Elastic Series D 2 USD 58m
Apr. 2016 MemSQL Series C 7 USD 36m Caffeinated, REV
Mar. 2016 Cockroach Labs Series A 4 USD 20m Index
Mar. 2016 Couchbase Series F 7 USD 30m Sorenson
Feb. 2016 NuoDB Series B 3 USD 17m
Source: Crunchbase
12 | THE NEW BIG DATA WORLD 13
Hadoop for big data: System or HDFS; and processing, computation and data are distributed FIG. 13: REVENUES OF CLOUDERA AND HORTONWORKS (2014-2018E) (USD M)
FIG 13
the rise and fall? the MapReduce programming model. via high-speed networking.
Hadoop splits files into large blocks
The initial release of Apache Hadoop An ecosystem of additional software
and distributes them across nodes in 500
took place in 2006. This distributed packages for managing data has
HORTONWORKS (FYE 31/12 N)
445.0
a cluster. It then transfers packaged 450 CLOUDERA (FYE 31/1 N+1)
file system is a collection of open- been developed around Hadoop
code into nodes to process the data 400
367.4
source software tools that facilitate (See Fig.12). 340.5
in parallel. This approach allows the 350
the use of computer clusters to solve 300
dataset to be processed faster and 261.0 261.8
issues involving massive amounts Hadoop can be deployed on-premise, 250
more efficiently than in a conventional
of data. Its core has two elements: or in a public or private cloud: 200
166.0 184.5
supercomputer architecture, which
storage, Hadoop Distributed File Microsoft Azure (with Microsoft Azure
150
109.1 121.9
uses a parallel file system where 100
HDInsight), Amazon EC2 and S3 50 46.0
(with Amazon Elastic MapReduce), 0
FIG. 12: THE HADOOP DATA MANAGEMENT ECOSYSTEM Google Cloud Platform (Google Cloud 2014 2015 2016 2017 2018E
Dataproc), SAP Cloud Platform (SAP Source: Cloudera; Hortonworks
Spark Cluster-computing framework for data analytics and machine-learning algorithms
Big Data Services), and Oracle Cloud
Hive Data warehousing software providing a SQL-like query language, HiveQL Platform (Oracle Big Data Cloud).
Non-relational distributed database for storing large quantities of sparse data, In October 2018, Cloudera Plummeting storage costs to load it into special storage. Other
HBase The main Hadoop software
for serving data-driven websites announced it was acquiring cloud database services - Snowflake,
distributions are Cloudera, On cloud storage services like AWS
Phoenix Massively parallel relational database based on SQL, using HBase as its data store Hortonworks through an all-stock Google BigTable, Amazon Aurora,
Hortonworks and MapR. Hortonworks S3, Azure Blob Storage, and Google
‘merger of equals’, leaving Cloudera and Microsoft Cosmos - are similarly
Impala Massively parallel SQL query engine for analytics
and Cloudera have been listed in the Cloud Storage, a terabyte of cloud
shareholders with 60% of the massive-scale, highly flexible,
Pig Platform for creating programmes on Hadoop US since December 2014 and April object storage costs about USD 20 a
combined company. The transaction globally distributed ‘pay-as-you-
2017, respectively. Their revenues month, compared to around USD 100
ZooKeeper Distributed computing system was completed in January 2019. go’ databases.
surged at over +40% per year until a month on Hadoop Distributed File
Flume Log data collection 2017, after which growth rates slowed We understand the deal as a signal System (HDFS). Python and R data science running
Data transfer between relational databases and Hadoop
in 2018, when the mid-point of the Hadoop market could no longer on containers
Sqoop Faster, better, and cheaper
company guidance implied +21% for sustain two big competitors, with
Oozie Workflow scheduling cloud databases Hadoop’s revolutionary innovation
Cloudera and +30% for Hortonworks. several trends driving the change:
was that it offered both a storage and
Storm Distributed stream processing computation framework Cloudera’s shares dropped 40% on The advent of cloud services like
compute environment for applications
4th April 2018 when it reported its The shift to public cloud Google BigQuery in 2011 eliminated
written in Java. However, this was out
FY18 results due to disappointing AWS, Microsoft Azure, and Google the need to run Hadoop or Spark.
of step with data scientists working
guidance for FY19. Cloud offer their own managed While Apache Spark is used to handle
on machine learning in Python and
Hadoop/Spark services as part of ad-hoc distributed SQL queries,
R, who need native deployment
fully integrated offerings that are Google BigQuery runs ad-hoc queries
of Python/R models to iterate and
cheaper to buy and scale. on any amount of data stored in its
improve machine learning models.
object storage service without having
14 | THE NEW BIG DATA WORLD 15NoSQL databases: There are four main categories of Key-value (KV) stores FIG. 14: CLASSIFICATION OF NOSQL DATABASES
simpler than SQL NoSQL database:
These databases use dynamic data
Wide column stores Document-oriented databases Key-value stores Graph databases
The concept of ‘NoSQL’ databases Wide column stores such as session data and machine-
emerged around 2009. The term was generated data used for analysis and Apache Accumulo (based on Google BigTable) Amazon DocumentDB Aerospike Database AllegroGraph
introduced by IT developer Johan Data is collected in very large systems and device coordination. Apache Cassandra (DataStax) Apache CouchDB Amazon DynamoDB Apache Giraph
Oskarsson to label a growing number structures for high-performance They treat the data as a single
of non-relational, distributed data analytics. By storing data in columns collection that may have different fields Apache HBase ArangoDB Apache Ignite ArangoDB
stores including Google BigTable, rather than rows, the database an for every record. Key-value databases Druid BaseX Apache ZooKeeper Objectivity InfiniteGraph
Amazon Dynamo and the newly access the data it needs to answer often use far less memory to store the
launched Amazon DocumentDB. a query more precisely, without the same data than relational databases,
Micro Focus Vertica Clusterpoint ArangoDB MarkLogic
Most early NoSQL systems did not need to scan and discard unwanted which can lead to large performance Couchbase Server Basho Riak Neo4J
attempt to provide the atomicity, data in rows. Wide column stores gains in certain workloads.
Elasticsearch Couchbase Server OpenLink Virtuoso
consistency, isolation and durability are well-suited for data warehouses
(ACID) guarantees that relational involving highly complex queries. Graph databases Microsoft Azure Cosmos DB FoundationDB SAP OrientDB
databases usually offer. NoSQL These are organised in a graph IBM Notes/Domino InfinityDB
Document-oriented databases
databases enable the storage and structure for relationship and pattern
MarkLogic Oracle BerkeleyDB
retrieval of data modelled in other These are used for managing semi- analysis, for example fraud detection
means than through tabular relations. structured data in document formats or semantic text analysis. The graph MongoDB SAP OrientDB
They are increasingly used in big such as JSON or XML. Document directly relates data items in the Qualcomm Qizx SciDB
data and real-time web applications databases store all the information store to a collection of nodes of
due to design simplicity, simpler for a given object in a single instance data and edges representing the RethinkDB
scaling to clusters of machines, in the database, and every stored relationships between the nodes. The SAP OrientDB
and finer control over availability. object can be different from every relationships allow data in the store to
The data structures used by NoSQL other. This makes mapping objects be linked together directly. Querying Source: Bryan, Garnier & Co
databases (key-value, wide column, into the database a simple task and relationships within a graph database
graph, or document) make some typically eliminates anything similar is fast because they are perpetually
operations faster in NoSQL than in to an object-relational mapping. This stored within the database itself.
relational databases. makes document stores attractive for Relationships can be intuitively
programming web applications, which visualised using graph databases,
are subject to continual changes, and making them useful for heavily
where speed of deployment is an inter-connected data.
important issue.
16 | THE NEW BIG DATA WORLD 17MongoDB was the first listed FIG 1515: MONGODB - REVENUES AND NON-GAAP OPERATING MARGIN (FYE 31/1)
FIG.
pure-play NoSQL database software
vendor, with a NASDAQ IPO in REVENUES (USD M) (LEFT SCALE)
NON-GAAP OPERATING MARGIN (%) (RIGHT SCALE)
October 2017. The company is still -36.2%
heavily loss-making, with a -49.2%
non-GAAP operating margin -64.0% -229.0
forecasted at -36% at the mid-point
of company guidance for FY19. -91.7%
Losses are reducing year-on-year
while revenues are growing around 154.5
50% annually.
101.4
The second NoSQL database
pure-player to be listed was Elastic,
which develops and sells the -168.1% 65.3
- 40.8
Elasticsearch document-oriented
database and search and analytics
FY15 FY16 FY17 FY18 FY19E
engine. Listed on NYSE in October
2018, this company is also loss- Source: MongoDB
making, with a non-GAAP operating
margin of -20% for the fiscal year
FIG 1616: ELASTIC - REVENUES AND NON-GAAP OPERATING MARGIN (FYE 30/4)
FIG.
ending 30th April 2018. Revenues,
on the other hand, grew 81% during REVENUES (USD M) (LEFT SCALE)
that fiscal year. NON-GAAP OPERATING MARGIN (%) (RIGHT SCALE)
255
159.9
-17.6%
-20.0%
88.2
-31.2%
FY17 FY18 FY19E
Source: Elastic; Bryan, Garnier & Co
18 | THE NEW BIG DATA WORLD 19NewSQL databases The issue with NoSQL databases There are three main categories FIG. 17: CLASSIFICATION OF NEWSQL DATABASES
is their lack of compliance with the of NewSQL database:
One of the reasons why relational
ACID rules when processing Shared-noting databases Storage engines for SQL Transparent sharding
databases have been so successful Shared-nothing (SN) databases
‘high-profile’ data (e.g. financial
for decades is that they have a set Altibase Infobright ScaleBase
data or orders) with strong These are designed to operate in
of characteristics that guarantee Apache Ignite MyRocks Vitess
transactional and consistency a distributed cluster of ‘shared-
their validity: Atomicity, Consistency,
requirements. NewSQL – a concept nothing’ nodes in which each node CockroachDB MariaDB Columnstore
Isolation, and Durability (ACID).
which emerged in 2011 from the is independent and self-sufficient,
Atomicity guarantees each Exasol Microsoft SQL Server (with ColumnStore and InMemory features)
451 Group analyst Matthew Aslett and owns a subset of the data,
transaction is treated as a single unit
– seeks to offer the same scalable while none of the nodes shares Google Spanner Oracle InnoDB
that either succeeds completely or
performance of NoSQL databases memory or disk storage. Some SN
fails completely. Consistency means GridGain Oracle MySQL Cluster
for transaction workloads while still database platforms are in-memory,
that any data written to the database
maintaining the ACID guarantees of a like Altibase, Apache Ignite, Exasol, HarperDB TokuDB
must be valid according to all defined
relational database. Both NoSQL and GridGain, MariaDB Clustrix, MariaDB Clustrix
rules – this prevents database
NewSQL support the relational data and VoltDB.
corruption by an illegal transaction, MemSQL
model and use SQL as their primary
but does not guarantee a transaction Storage engines for SQL
interface. Applications typically Microsoft Azure Cosmos DB
is correct. Isolation ensures that
targeted by NewSQL databases Optimised storage engines for SQL,
concurrently executed transactions NuoDB
have a large number of short-lived, these databases providing the same
leave the database in the same state
repetitive transactions that touch a programming interface as SQL but Trafodion
which would have been obtained
small subset of data. Unlike NoSQL scale better than built-in engines.
if the transactions were executed VoltDB
databases, NewSQL databases are
sequentially. Finally, durability Transparent sharding
not easy to categorise by technical Source: Bryan, Garnier & Co
guarantees that once a transaction
approach as they vary significantly These NewSQL databases provide
has been committed, it will remain
in their internal architecture. a horizontal data micro-partition
committed even in the case of a
Some support only transactional layer to automatically split databases
power outage or crash.
workloads, but others support hybrid across multiple nodes.
transactional/analytical workloads,
which some NoSQL databases
support too.
20 | THE NEW BIG DATA WORLD 21Cloud data warehousing While the on-premise data
As for access, Amazon and
warehousing software market
An increasing number of data Google provide a fully managed
is dominated by Oracle, IBM,
warehouses are now available in the data warehousing service that
Microsoft, Teradata and SAP, the
cloud rather than on-premise. Cloud is available exclusively on their
cloud data warehouse market is led
data warehouses have the advantage own public cloud infrastructure.
by Amazon Web Services (Redshift),
of being provisioned in minutes and Oracle’s cloud data warehouse is
Snowflake, Google (BigQuery),
without any technical expertise. only available within the Oracle
and Oracle (Autonomous Data
They suit small and mid-sized Cloud. IBM is available as a
Warehouse Cloud).
companies, business analysts, and managed cloud service on IBM’s
other non-technical users who want Challengers in this market include cloud infrastructure (SoftLayer),
to access, store and process large Teradata (IntelliCloud), IBM (Db2 although it can also be deployed
amounts of data at low cost. Initially Warehouse on Cloud), Microsoft on all major cloud providers with
focused on simple data loading and (Azure SQL Data Warehouse), and the potential ‘downside’ of the
reporting, cloud data warehouses Cloudera (Hortonworks HDP). Smaller customer having to manage the
now support complex cases such players exist, like MarkLogic, Pivotal, software themselves. However,
as IoT analytics, real-time analytics, Exasol and Yellowbrick Data, while most of the other independent
fraud analysis and 360-degree some large players like Alibaba, Micro players have a multi-cloud
customer views at the hundreds of Focus (Vertica) and Huawei have a approach, using Amazon Web
terabyte (and even petabyte) scale. weak presence. Services, Microsoft Azure, Google
And while cloud data warehouses Cloud and/or their own data
were initially only deployed for new centers to run their software.
projects, some organisations are Finally, Yellowbrick Data - which
migrating their on-premise data exited stealth in July 2018 and
warehouses to the cloud, whether it since then has raised USD 92m -
is a public, private or hybrid cloud. has its data warehouse available
on-premise, on private clouds, on
colocation and on the edge (IoT),
and plans to be available
on public clouds in 2019.
22 | THE NEW BIG DATA WORLD 233. Cloud-based data warehousing:
the Snowflake case
Snowflake, database performance efforts for its FIG. 18: SNOWFLAKE COMPUTING – SUMMARY OF FUNDING ROUNDS In January 2018, CEO Bob Muglia
a ‘superfunded’ company parallel systems. Thierry Cruanes publicly stated that the USD 263m
“In many cases, Snowflake is spent 13 years at Oracle, working on round would probably be the last
the only product that’s viable The cloud-based data warehousing
the optimisation and parallelisation Date Lead investor(s) Other investors Capital raised/valuation before a possible IPO. At that time,
for customers to move to. software vendor Snowflake
layers in Oracle databases. Before the company had more than 1,000
Customers demand a level of Computing is, in our view, a good Redpoint ventures
Oracle, he spent seven years at the June 2015 Altimeter Capital Sutter Hill USD 71m customers, up from 450 in April
scale that Amazon Redshift example of the new generation Wing VC
IBM and Sema, working on data 2017, and expected this to double
just can’t do. We can scale of successful data management Iconiq Capital
mining techniques. Finally, Marcin April 2017 Existing investors USD 105m over 2018. Snowflake employed 330
to a large number of queries. companies that have emerged from Madrona Venture Group
Zukowski was co-founder and CEO staff, up from 175 in April 2017, and
Because we’re just a very large the cloud revolution. The company Iconiq Capital
of Vectorwise, an analytical database January 2018 Altimeter Capital USD 263m/USD 1.5bn expected this to also almost double,
cloud service, we throw the develops and sells a cloud-based Sequoia Capital
software vendor sold to Actian in to 600, during 2018. The two large
power of the cloud at running storage and analytics platform, the
2010. During its first two years of October 2018
Sequoia Capital
Existing investors
USD 450m/USD 3.5bn funding rounds in 2018 was aimed
things at the same time. Every Snowflake Elastic Data Warehouse (pre- IPO) Meritech Capital
existence, Snowflake operated in helping Snowflake in four areas:
single day, we run almost 20 (EDW), which uses a cloud-based
stealth mode, looking to create data
million queries a day.” infrastructure and has been available Source: Crunchbase 1. continuing to expand its multi-
warehouse software designed for FIG 19
since June 2015. As a testimony to cloud strategy beyond Amazon
the cloud.
Bob Muglia, CEO, Snowflake its success, large companies like Web Services and Microsoft Azure;
Computing. Capital One, Nielsen, and Adobe use FIG. 19: SNOWFLAKE COMPUTING - TOTAL FUNDING AND VALUATION (USD M)
The company came out of stealth
Snowflake EDW. New customers in mode in 2014, just after the 2. growing sales teams across and
4,000 TOTAL VALUATION
2018 include leading brands such as appointment of CEO Bob Muglia. At outside the US to meet increasing
3,500
FUNDING ROUND 3,500
Netflix, Office Depot, Netgear, and Microsoft between 1988 and 2011, he demand for the only cloud-built
Yamaha. In our view, Snowflake’s had worked as the product manager
3,000 data warehouse;
success can be attributed to the for SQL Server, Vice-President 2,500
strategic vision of its founders, the 2,000 3. investing further in Snowflake’s
Windows NT, Server Application
involvement of its CEO Bob Muglia, 1,500
1,500 data warehouse-as-a-service by
Group and .NET Services Group,
and a successful funding series. 1,000 growing its engineering team in
then Senior Vice-President Servers &
Tools. Then at Juniper Networks he 500
263 450 San Mateo, CA, Bellevue, WA,
Snowflake Computing was founded 26 45 105 and Berlin, Germany);
served as EVP Software & Solutions 0
in 2012 in San Mateo, California, 2014 JUN 2015 APR 2017 JAN 2018 OCT 2018
(2011-2013).
by CTO Benoît Dageville, Thierry 4. delivering innovations such
Source: Snowflake company data
Cruanes (Product Architect) and as Snowflake Data Sharing, also
In June 2015, Snowflake announced
Marcin Zukowski. Benoît Dageville known as ‘The Data Sharehouse’,
the general availability of Snowflake
spent 15 years at Oracle as the which further consolidates its
EDW, as well as the first in a series of
lead architect for parallel execution lead over legacy cloud and
funding rounds (See Fig. 18 and 19).
and a key architect in the SQL on-premise solutions.
Manageability group. Before Oracle,
he worked at Bull, where he helped
define the architecture and lead
24 | THE NEW BIG DATA WORLD 25Snowflake’s approach: The Snowflake EDW platform has the FIG. 20: SNOWFLAKE EDW PRODUCT ARCHITECTURE
100% ‘as-a-service’ following features:
Snowflake’s data warehouse-as- • The cloud storage layer is
a-service uses the SQL language engineered to scale independently AD-HOC ANALYTICS
to manage data. It can work with a of compute resources. It can TRANSACTIONS SECURITY
mix of structured data such as CSV process data loading and unloading
files and tables, as well as semi- without impacting running queries.
structured data like JSON, Avro, XML Snowflake uses micro-partitions MANAGEMENT METADATA
and Parquet. It uses the Amazon (‘sharding’) to store customer data,
Web Services S3 service as a cloud which are compressed in columns
infrastructure. Since September 2018, and encrypted.
LOADING
CENTRALISED DEVELOPMENT
Snowflake has also been generally
available on Microsoft Azure, • On the compute layer, data
STORAGE
OPTIMISATION METADATA
integrated with Azure services such processing is performed by virtual
as Microsoft Azure Data Lake Store warehouses, which retrieve the
and Microsoft Azure Power BI. The data required from the storage
platform also employs several new Source: Snowflake Computing, company data
layer to satisfy queries, then
features, for example limitless storage cache it locally with computing
The company was initially competing Bob Muglia’s long-term vision for
accounts, accelerated networking resources along with the caching
with Amazon Redshift, another Snowflake is that the company’s
and storage soft delete. As of today, of query resources to improve the
cloud-based data warehousing data lake technology becomes
Snowflake is available in the Azure performance of future queries.
system. However, as an increasing a platform for building data
East-US-2 region (in Virginia), with Multiple data warehouses can
number of large firms move towards applications. This could be for data
more regions to be added in the simultaneously operate on the
cloud adoption and look for solutions sharing between customers or to sell
future3. Whether it is running on same data.
that can handle structured, semi- that data. He foresees companies
Amazon Web Services or Microsoft
structured and transactional data, building businesses on top of the
Azure, from the US and Dublin, • The services layer authenticates
Snowflake has been increasingly data stored in the Snowflake data
Snowflake generally costs USD 40 user sessions, manages,
competing with Oracle, IBM and warehouse much as companies
per terabyte of data per month for enforces security, performs query
Teradata. The company often picks have built businesses on top of the
on-demand storage, and USD 23 compilation and optimisation,
up customers disillusioned with Salesforce platform.
per terabyte per month for capacity and coordinates transactions.
Hadoop, and it benefits from the
storage. From Frankfurt, pricing is It uses a distributed metadata
IoT wave as the semi-structured
USD 45 and USD 24.50 respectively. store for global state management.
data generated by sensors is
Metadata processing is powered
complicated to manage with a
by a separate integrated
structured approach.
sub-system, which allows data
processing without using query
computer resources.
3
Microsoft Azure has at least 14 cloud regions in the US and 54 globally.
26 | THE NEW BIG DATA WORLD 274. In-memory analytics database:
the Exasol case
Exasol, a hidden champion Exasol has offices in Germany, The main memory served as a large
France, the UK and the US, and sells cache, from which content was placed
“Exasol’s vision is to provide Headquartered in Nuremberg
its products through partners in the in local storage when it was not
the most extensible, powerful (Germany), Exasol develops and
rest of Europe, Israel and the USA, efficient to fit everything into the cache.
and platform independent sells a high-performance, in-memory,
but also directly to customers in India, That way, it was possible to analyse
data analytics framework for column-oriented relational database
Australia and South America. As of more data even more quickly than with
customers to govern, connect management and data warehousing
today, more than 140 companies a pure in-memory database. Current
and understand the entirety of system specifically designed
use Exasol to run business main shareholders are board members,
the data flowing through their for analytics. It launched its first
intelligence and data analytics, founders, the German State-owned
organisations. As they transition product in 2005. Either standalone
including well-known names such as bank KfW and the Swiss early-stage
to data-driven processes, or integrated with Hadoop, Exasol
Adidas, Zalando, GfK, King Digital venture firm Mountain Partners.
companies need to see and is used in a wide range of Big Data
Entertainment, Vodafone, Xing,
leverage all the data necessary use cases, including accelerating
Revolut, Dailymotion, and Postbank.
for the success of their business, standard reporting, running multi-user FIG. 21: EXASOL PRODUCT ARCHITECTURE
wherever the data may be. For ad-hoc analytics, and performing Exasol’s technology was created by
this they will need an incredibly complex modelling. Exasol’s ability one of its founders, Falko Mattasch, APPLICATIONS
powerful and intelligent data to pull in data in real-time allows in the 1990s. While researching
framework which will permit end-users to compress the multi- intelligent algorithms in the Parallel REAL-TIME
new tools, technologies and step process of data collection and Processing Department at the
DATA ADVANCED PREDICTIVE
AD-HOC
BUSINESS LOGICAL DATA
SCIENCE ANALYTICS ANALYTICS INTELLIGENCE WAREHOUSE
processes to be implemented pre-processing into a rapid execution University of Jena (Germany), he
REPORTING
on demand, as smoothly and as analytics engine. This means users showed how data analytics could
simply as possible.” can perform quick ad-hoc analysis benefit from parallel processing. After
and answer critical business leaving academia, he implemented a
Aaron Auld, CEO, Exasol questions in minutes. new prototype for a parallel database ANALYTICS
system, which served as the basis for
Exasol’s core differentiation lies in its
the technology behind Exasol. It took
proprietary technology architecture
years for the company to develop a SQL MAP REDUCE R PYTHON JAVA LUA SKYLINE GEOSPATIAL GRAPH
that combines in-memory plus MPP
complete database, as everything
(Massive Parallel Processing) which
was created in-house without
results in extreme high querying EXASOL – THE PARALLEL IN-MEMORY DATABASE
recourse to open source database
speed at large data volumes. Their
cores such as PostgreSQL. This led
platform integrates with a wide range
to a software system that offered
of front-end analytics tools, such as
optimum performance and was
Tableau and Microstrategy.
highly scalable and ‘tuning-free’. As EXASTORAGE
HADOOP SYSTEMS
DATABASES CLOUD STORAGE
data was continuously changed and
updated, Exasol designed the system
so that not all data had to be stored DATA
in the main memory.
Source: Exasol company data
28 | THE NEW BIG DATA WORLD 295. Conclusion
In this report, we have reviewed Database is adding resilience. As for market consolidation, we
the deep changes happening in Oracle also has the financial might saw a significant wave of M&A
the database world, and the new to acquire cloud database or data deals between 2008 and 2013,
generation of players aiming to warehouse players if they add value before the advent of NoSQL and
challenge longstanding leaders such to its offerings and for shareholders. NewSQL databases and cloud data
as Oracle, Microsoft and IBM. The So, while it’s not dying yet, its warehouses. Many small database
question now is how the competitive competitive position is not as players have emerged since the
landscape will be reshaped ten years comfortable as it used to be. beginning of the decade, with only
on. Will Oracle remain as the #1 a handful achieving significant scale
database player? Can Amazon Web AWS can steal the #1 position in or funding. Nonetheless, some have
Services be the new #1 in this market? databases. But with a market share become ‘unicorns’, most recently
Is Hadoop definitely a fallen ‘star’? is at 9%, it could take a long time to Snowflake Computing in data
How the market will consolidate? get there. However, Amazon’s growth warehousing. We believe there will
What could be the next database comes from the cloud: for every cloud be an opportunity for incumbent
‘unicorns’? Let’s try to address customer it gets, there is a potential database companies to acquire
these questions. customer for DynamoDB, Aurora and some of these players to stay
Redshift. That said, its database and competitive with AWS. As Elastic
Oracle, in our view, will be increasingly data warehouse offerings are tied to its and MongoDB demonstrated, EV/
challenged by Amazon Web Services, cloud, so a Microsoft Azure or Google sales valuation multiples can be very
but this does not mean it will lose its Cloud customer will not use AWS rich. Consequently, adding a sizeable
leadership in the database market. database or data warehouse services. premium to the valuation of the next
Recently, AWS CEO Andy Jassy IPOs in this space is tempting.
declared that by the end of 2018 Hadoop is losing ground because
or mid-2019, 88% of Amazon’s of its complexity but is not dead in Finally, who are the new unicorns? In
databases that ran on Oracle would our view. The 2019 merger between data management, it’s now MongoDB,
be on an Amazon database instead. Cloudera and Hortonworks means Elastic and Snowflake. There are
He also noted that Amazon moved its growth is unlikely to be as strong as plenty of potential unicorns out
data warehouse from Oracle to its own expected. We think that independent there too: we think Neo4j, DataStax
service, Redshift in early November players may well disappear, probably (Cassandra), Databricks, Cockroach
2018. SAP’s long-term ambition is through M&A. However, almost all the Labs, MemSQL, Yellowbrick Data,
to replace Oracle databases running large software vendors have made Couchbase, and MariaDB could be the
on its application software with significant investment in Hadoop open next ones. In Europe, there are only
SAP HANA. Other defections may source technologies, which suggests a few players, such as Elastic in The
follow. But Oracle has the strength it is here to stay and will probably Netherlands and MariaDB in Finland,
of an installed base (several hundred evolve, supported by the open source but Exasol, a German company, has
thousand of customers) which is not Apache Software Foundation. potential to benefit from the cloud data
fully dependent on Amazon or SAP. warehouse wave as its technology is
Oracle’s databases are also evolving, based on a solid and powerful
and the new Oracle Autonomous in-memory database.
30 | THE NEW BIG DATA WORLD 31White Paper Authors Corporate Transactions
Falk Müller-Veerse Gregory Ramirez
Partner Equity Research Analyst Bryan, Garnier & Co leverage in-depth sector expertise to create fruitful and long lasting relationships between investors and
Technology Software & IT Services European growth companies.
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Investment Banking Investment Banking
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