Examining Top Data Analytics Firms in the 2019 Forbes Cloud 100

As part of the selection process for the list, Forbes’ data partner, Bessemer Venture Partners, received hundreds of submissions from the top cloud …

Examining Top Data Analytics Firms in the 2019 Forbes Cloud 100

The 2019 Forbes Cloud 100 recognizes the best private cloud companies in the world. Ranging from small startups to venture-backed cloud behemoths, the fourth annual listing makes mention of popular consumer-facing companies like Stripe, Mailchimp, Toast and others. As part of the selection process for the list, Forbes’ data partner, Bessemer Venture Partners, received hundreds of submissions from the top cloud startups. A panel of judges selected the providers based on four factors: estimated valuation (30%), operating metrics (20%), people & culture (15%) and market leadership (35%), which the judge panel then weighed to select, score and rank the winners.

The 2019 listing isn’t just made up of consumer technology brands, however. The popular award directory also includes a growing number of enterprise technology and software providers. The editors at Solutions Review have perused the 2019 Forbes 100 and identified these top data analytics firms as warranting extra attention. Companies are listed in the order Forbes has them ranked.

Our Buyer’s Guide for Analytics and Business Intelligence Platforms helps you evaluate the best solution for your use case and features profiles of the leading providers, as well as a category overview of the marketplace.

5. Datadog

Datadog offers a monitoring service that brings together metrics and events from servers, databases, applications, tools and services to present a unified view of the infrastructure. Capabilities are delivered via an SaaS-based data analytics platform that enables Dev and Ops teams to work closely on the infrastructure to resolve performance issues and ensure that development and deployment cycles finish on time. It was recently revealed that the New York-based company set its initial price range for an upcoming IPO.

13. Databricks

Databricks offers a unified analytics platform that allows users to prepare and clean data at scale and continuously train and deploy machine learning models for AI applications. The product handles all analytic deployments, ranging from ETL to models training and deployment. It is also available as a fully managed service on Microsoft Azure and Amazon Web Services. Reference customers rank Databricks highly for its end-to-end analytics lifecycle and support and accessibility for a variety of data science use cases. Databricks has had a big year, highlighted by its $250 million-dollar funding round in February and a major product update last month.

37. Sisense

Sisense makes it easy for organizations to reveal business insight from complex data in any size and from any source. The data analytics product also allows users to combine data and uncover insights in a single interface without scripting, coding or assistance from IT. Sold as a single-stack solution with a back-end for preparing and modeling data, Sisense features an expansive portfolio of analytic functionality and a front-end for dashboarding and visualization. In addition to a slew of new product capabilities, Sisense’s major play this year was its May acquisition of Periscope Data.

59. ThoughtSpot

ThoughtSpot’s artificial intelligence-driven analytics platform features what the company dubs the world’s first relational search engine. The tool combines relational search with a custom-built, in-memory relational data cache to speed up queries that are run over many lines of data. ThoughtSpot connects with any on-prem, cloud, big data, or desktop data source. The company’s August $248 million-dollar funding round brings ThoughtSpot’s total raised to $554 million since its founding in 2012. ThoughtSpot was named the first new leader in six years in the Gartner Magic Quadrant for Analytics and BI Platforms as well.

96. Dataiku

Dataiku offers an advanced analytics solution that allows organizations to create their own data tools. The company’s flagship product features a team-based user interface for both data analysts and data scientists. Dataiku’s unified framework for development and deployment provides immediate access to all the features needed to design data tools from scratch. Users can then apply machine learning and data science techniques to build and deploy predictive data flows. Dataiku’s popular data science platform has helped the company to reportedly double in size over the last 18 months.

See the full Forbes Cloud 100 list.

Timothy King
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Timothy King

Senior Editor at Solutions Review
Timothy is Solutions Review’s Senior Editor. He is a recognized thought leader and influencer in enterprise BI and data analytics. Timothy has been named a top global business journalist by Richtopia. Scoop? First initial, last name at solutionsreview dot com.
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Top 8 Cloud Data Warehouses

These top cloud data warehouses demonstrate the attributes that have grown … data warehouse architecture for distributing queries and data analysis. … looking to use standard SQL queries to analyze large data sets in the cloud.
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A clouddata warehouse is a service that collects, organizes and often stores data that is used by organizations for different activities including data analytics and monitoring.

With a cloud data warehouse, the physical aspects are all with cloud companies. They are abstracted for end users that just see a large, warehouse or repository of data waiting and available to be processed. The market for cloud data warehouses has grown in recent years, as organizations move to take advantage of cloud economics and reduce their own physical data center footprints.

Cloud data warehouses typically include a database or pointers to a collection of databases, where the production data is collected. The second core element of many modern cloud data warehouses is some form of integrated query engine that enables users to search and analyze the data. This assists with data mining.

How To Choose a Cloud Data Warehouse Service

When looking to choose a cloud data warehouse service there are a number of criteria that organization should consider.

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Existing cloud deployments. Each of the major public cloud providers has its own data warehouse that provides integration with existing resources, which could make deployment and usage, easier for cloud data warehouse users.

Ability to migrate data. Consider the different types of data the organization has and where it is stored. The ability to migrate data effectively into a new data warehouse is critically important.

Storage options. While data warehouse solutions can be used to store data, having the ability to access commodity cloud storage services, can provide lower cost options.

In this Datamation top companies list, we spotlight the vendors that offer the top cloud data warehouse services.

Amazon Redshiftaws

Value proposition for potential buyers. As Amazon’s entry in the cloud data warehouse market, Redshift is an ideal solution for those organizations that have already invested in AWS tooling and deployment.

Key values/differentiators:

  • A key differentiator for Redshift is that with its Spectrum feature, organizations can directly connect with data stores in the AWS S3 cloud data storage service, reducing the time and cost it takes to get started.
  • One of the benefits highlighted by users is Redshift’s performance, which benefits from AWS infrastructure and large parallel processing data warehouse architecture for distributing queries and data analysis.
  • For data that is outside of S3 or an existing data lake, Redshift can integrate with AWS Glue, which is an extract, transform, load (ETL) tool to get data into the data warehouse.
  • Data warehouse storage and operations are secured with AWS network isolation policies and tools including virtual private cloud (VPC).

Google BigQueryGoogle

Value proposition for potential buyers. BigQuery is a reasonable choice for users that are looking to use standard SQL queries to analyze large data sets in the cloud.

Key values/differentiators:

  • As a fully managed cloud service, setup of the data warehouse and resource provisioning are all handled by Google, using serverless technologies.
  • The ability to easily query data with either SQL or via Open Database Connectivity (ODBC), is a key value of BigQuery enabling users to use existing tools and skills.
  • Logical data warehousing capabilities in BigQuery lets users connect with other data sources including databases and even spreadsheets to analyze data.
  • Integration with BigQuery ML is a key differentiator, bringing the worlds of data warehouse and Machine Learning (ML) together. With BigQuery ML machine learning workloads can be trained on data in a data warehouse.

IBM Db2 Warehouseibm

Value proposition for potential buyers. IBM Db2 Warehouse is a strong option for organizations that are handling analytics workloads that can benefit from the platform’s integrated in-memory database engine and Apache Spark analytics engine.

Key values/differentiators:

  • Integrates the Db2 in-memory, columnar database engine, which can be a big benefit for organizations looking for a data warehouse that includes a high-performance database.
  • Apache Spark engine is also integrated with Db2, which means that users can use both SQL as well as Spark queries, against the data warehouse to derive insights.
  • Db2 Warehouse benefits from IBM’s Netezza technology with advanced data lookup capabilities
  • Cloud deployment can be done in either IBM cloud or in AWS, and there is also an on-premises version of Db2 Warehouse, which can be useful for organizations that have hybrid cloud deployment needs.

Microsoft Azure SQL Data Warehousemicrosoft

Value proposition for potential buyers. Microsoft Azure SQL Data Warehouse is well suited for organizations of any size, looking for an easy on-ramp into cloud-based data warehouse technology, thanks to integration with Microsoft SQL Server.

Key values/differentiators:

  • Microsoft released a major update for Azure SQL Data Warehouse in July 2019, with the Gen2 update, providing more SQL Server features and advanced security options.
  • Dynamic Data Masking (DDM) provides a very granular level of security control enabling sensitive data to be hidden on the fly as queries are made.
  • Existing Microsoft users will likely find the most benefit from Azure SQL Data Warehouse, with multiple integrations across the Microsoft Azure public cloud and more importantly SQL Server for database.
  • In contrast to simply running SQL Server on-premises, Microsoft has built on a massive parallel processing architecture that can enable users to run over a hundred concurrent queries at the same time.

Oracle Autonomous Data Warehouseoracle

Value proposition for potential buyers. For existing users of the Oracle database, the Oracle Autonomous Data Warehouse might be the easiest choice, offering a connected onramp into the cloud.

Key values/differentiators:

  • A key differentiator for Oracle is that it is running the Autonomous Data Warehouse in an optimized cloud service running Oracle’s Exadata hardware systems, which have been purpose built for Oracle database.
  • The service integrates a web based notebook and reporting services to share data analysis and enables easy collaboration.
  • While Oracle’s own namesake database is supported, users can also migrate data from other databases and clouds, including Amazon Redshift, as well as on-premises object data stores.
  • Oracle’s SQL Developer feature is an other key feature, which integrates data loading wizard as well as a database development environment.

SAP Data Warehouse CloudSAP

Value proposition for potential buyers. SAP’s new Data Warehouse Cloud might be a good fit for organizations looking for more of a turnkey approach to getting the full benefit of a data warehouse thanks to pre-built templates.

Key values/differentiators:

  • Data Warehouse Cloud is a relatively new entrant in the space and was first announced at the 2019 SAPPHIRE NOW conference in May.
  • SAP’s HANA cloud services and database are at the core of Data Warehouse Cloud, supplemented by best practices for data governance and integrated with a SQL query engine.
  • A key differentiator for the platform is the integration of pre-built business templates that can help solve common data warehouse and analytics use-cases for specific industries and lines of business.
  • For existing SAP users, the integration with other SAP applications means easier access to on-premises as well as cloud data sets.

Snowflakesnowflake

Value proposition for potential buyers. Snowflake is a great option for organizations in any industry that want a choice of different public cloud providers for data warehouse capabilities

Key values/differentiators:

  • A key differentiator is Snowflake’s columnar database engine capability that can handle both structured and semi-structured data such as JSON and XML.
  • The decoupled Snowflake architecture allows for compute and storage to scale separately, with data storage provided on the user’s cloud provider of choice.
  • The system creates what Snowflake refers to as virtual data warehouse, where different workloads share the same data, but can run independently.
  • Queries are made via standard SQL, for analytics, with integration with both the R and Python programming languages.

Cloud Data Warehouse Comparison Chart

Features

Key Differentiator

Amazon Redshift

High-performance and massively parallel processing capabilities.

Network isolation security.

Direct integration with S3 cloud storage.

Google BigQuery

Part of Google Cloud.

Full SQL query support.

Integration with BigQuery ML for machine learning workloads.

IBM Db2 Warehouse

Includes an in-memory columnar database.

Cloud deployment options include both IBM Cloud as well as AWS.

Integrated support for Apache Spark data analytics.

Microsoft Azure SQL Data Warehouse

Data masking security capabilities.

Integrated with broader Azure cloud services.

Inclusion of Microsoft SQL Server support.

Oracle Autonomous Data Warehouse

Based on the latest Oracle Autonomous Database release.

Migration support for other databases and cloud data warehouse services.

Delivered by purpose-built Oracle Exadata hardware.

SAP Data Warehouse Cloud

Pre-built business templates.

Integration with existing SAP apps and services.

Based on SAP HANA database.

Snowflake

SQL based queries for analytics.

Support for JSON and XML as well as structured data.

Multi-cloud deployment options.

Microsoft and hubii to Officially Announce Ethereum Scaling Solution nahmii

Nahmii, a new scaling solution focused on making Ethereum ready for enterprise-level application development, will be revealed at an upcoming …

Nahmii, a new scaling solution focused on making Ethereum ready for enterprise-level application development, will be revealed at an upcoming blockchain event co-hosted by hubii and Microsoft.

Jacobo Toll-Messia, the CEO of hubii, the developer of nahmii, says:

‘Working closely with Microsoft will give nahmii the platform and exposure it needs to become the premier blockchain scaling and interoperability solution.

Our upcoming event, along with NBX and StartupLab, is a fantastic opportunity to demonstrate the power of nahmii to Microsoft’s existing customer base. This announcement is a major coup for hubii.”

Meanwhile, Microsoft’s director for Cloud and artificial intelligence Christopher Frenning states:

“The nahmii solution addresses a variety of issues related to the blockchain industry. We are excited to see protocols like nahmii add value by integrating their solutions with Microsoft Azure.

The deep product integration with nahmii is the type of technological solution that ensures our sales reps are in the best position to succeed in every interaction.”

The event, titled “Microsoft and hubii – Blockchain in Practice,” is scheduled to take place on September 12 at Microsoft’s Norway headquarters in Oslo, according to a press release shared with Crowdfund Insider.

Event participants will have the opportunity to learn about distributed ledger technology (DLT) platforms being developed by Microsoft, hubii, and various other companies.

Hubii will be officially announcing the launch of nahmii, which is powered by Microsoft Azure.

The nahmii protocol will serve as an interoperability and scaling solution that may enhance the performance and utility of DLT networks, according to the release.

Based on the Ethereum blockchain, Nahmii is reportedly compatible with smart contract-enabled DLT platforms. The protocol’s developers are planning to deploy Nahmii to the Bitcoin blockchain through RSK, a smart contract platform secured by the BTC network.

Nahmii has been designed to increase throughput, provide “near-zero” latency, maintain “near-instant” transaction finality while offering consistently low transaction fees. Some of the main use cases for Nahmii include enhancing gaming applications, internet of things devices, trading, and content distribution.

Toll-Messia is expected to go over hubii’s origins during the event. He will also provide more details regarding nahmii’s features, potential applications, roadmap, and the nahmii Foundation.

In 2014, Microsoft became one of the world’s first major adopters of cryptocurrency when the tech giant began taking Bitcoin payments for certain apps and digital content.

Microsoft has also been working on enterprise blockchain tools and applications. The multinational tech firm has released several open-source DLT products, in order to improve the capabilities of its cloud computing platform Azure.

In recent years, Microsoft has launched Azure Blockchain Workbench and the Azure Blockchain Services, which simplify the process of building applications on DLT platforms.

Azure-based Nahmii will allow Microsoft’s partners and clients to develop software solutions that make blockchain-based systems accessible to businesses throughout the world.

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Telstra Data Hub aims to boost data collaboration

The idea is that Telstra can be a trusted intermediary, and that participants can choose exactly which of their peers can access particular data.

The problem with directly integrating one system with another is that it isn’t scalable. Well, connecting one to another is simple, but the number of point to point connections grows faster than the number of systems connected.

So decades ago, organisations looking to connect their in-house systems turned to approaches such as the enterprise service bus, so each system could be connected to a common intermediary.

Telstra Data Hub can play a similar role for connectivity outside the organisation.

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The idea is that Telstra can be a trusted intermediary, and that participants can choose exactly which of their peers can access particular data.

For example, a retail chain may use multiple logistics company, and each logistics company may serve multiple retailers. But Telstra Data Hub can be used in such a way to prevent the various retailers and logistics providers from seeing their competitors’ data.

Participants’ data can be augmented with data from third parties, said Telstra Data Hub product owner Julian Butler, and the open ecosystem will provide access to analytics and other tools.

Telstra Connected Supply Platform

Telstra Data Hub runs on Azure Cloud, and was co-developed with Microsoft.

“We are combining the unique elements of Telstra’s largest, fastest and most reliable mobile network and growing 5G coverage with Microsoft Azure, our Intelligent Edge capabilities and our global expertise in leveraging technology to empower every person and every organisation on the planet to achieve more,” said Microsoft Australia chief partner officer Rachel Bondi.

The initial focus is on supply chain, water management, and agribusiness.

Supply chain “is a great example,” according to Butler, because there are so many links and there is so much value to be derived from knowing where an item is, what condition it is in, and where it came from.

iMove CRC managing director Ian Christensen said there are some 350 different freight data sets in Australia, but they are all siloed and not connectable. This means there is “knowledge, but no good way to bring it together,” he said.

Furthermore, “most supply chains aren’t 100% digitised,” warned Linfox CIO Conrad Harvey.

Grant Thornton Consulting partner Ian McCall warned that compliance concerns could put a brake on data collaboration, as revealing certain operational data could be deemed to be in breach of ASX rules.

And while everyone is trying to squeeze value out of their data, there is a possibility that the dominant player in a supply chain may try to appropriate all the value arising from such sharing.

Telstra Data Hub is also being applied to water quality on the Great Barrier Reef, and to water use in the Murray Darling Basin.

Other areas under consideration include intelligent transport, smart cities, and smart spaces, said Butler.

According to Telstra group executive for product and technology Christian Von Reventlow, Telstra Data Hub could yield “more than $100 billion in incremental value to customers and the economy through digitisation and data driven collaboration,” and in addition there is “a huge opportunity” to scale globally.

McCall was more pessimistic: “the world is going to dictate what Australia is going to do.”

Telstra Data Cloud already has a small number of paying customers – “at least five” – even though it is not yet generally available, said Von Reventlow.

“People understand why this makes sense.”

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Telstra, Microsoft launch Data Hub for enterprise data sharing

Australian operator Telstra has teamed up with Microsoft to secure access to data from diverse, even siloed, sources. The partnership seeks to support …
Australian operator Telstra has teamed up with Microsoft to secure access to data from diverse, even siloed, sources. The partnership seeks to support collaboration among organisations and across supply chains. The deal combines Telstra’s data sharing and collaboration platform with Microsoft’s Azure platform to create the Telstra Data Hub. The partnership will initially focus on three key areas – water use and management, agribusiness, and connected supply chain.

The water use and management service is currently being deployed to monitor water quality in Queensland’s Lower Burdekin River.

The Telstra Data Hub is being collaboratively developed with Microsoft and its Azure Cloud platform to allow companies to use, analyse and solve problems using the data they generate. The modular, cloud-based platform is designed to allow companies to securely share, view and exchange data to boost their productivity.

The Telstra Data Hub is designed to secure sharing data between data producers (citizens, businesses, government bodies and IoT-enabled ‘things’) and aggregating it across sectors and value chains.

Data sharing and analysis could help farmers boost their profitability and yields, and reduce the environmental impact by harnessing new data sources – many connected via Telstra’s growing 5G network. According to Telstra, national water assets could be more sustainable thanks to real time usage and pollution monitoring using IoT devices, while supply chains could be streamlined by the data concerning transport movements and utilisation.

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