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I recently attended Oracle OpenWorld for the first time in several years. The message at this year’s event was clear: Oracle is all in on the cloud. I had heard the message, but I didn’t get the full impact until I arrived at the Moscone Center in San Francisco. All signage at the event contained the word “cloud,” and Oracle issued 18 press releases in conjunction with OpenWorld related to cloud computing. I also found out that Oracle has its own definition of “cloud.”

Oracle now offers cloud services ranging from infrastructure as a service, which competes with Amazon Web Services, to database as a service to big data as a service to analytics as a service. These are in addition to Oracle applications offered in software-as-a-service configurations. Some years ago Larry Ellison expressed public resistance to “cloud computing”, but since then Oracle has been steadily investing in, adopting and now fully embracing it. Oracle’s direction reflects what our benchmark research has been showing for years: Cloud computing is being adopted ever more widely. For example, our Data and Analytics in the Cloud research shows that nearly half (48%) of organizations use cloud-based analytics today and virtually all (99%) expect to use cloud-based analytics eventually. The research also shows that one in four (24%) have the majority of their data in the cloud today and 86 percent expect the majority of their data to be in the cloud eventually.

In the big data and analytics market, Oracle offers the following cloud services:

  • Oracle Exadata Cloud Service – massively parallel processing (MPP) SQL database
  • Oracle Business Intelligence Cloud Service – business intelligence and visualization
  • Oracle Big Data Cloud Service – Cloudera Enterprise (Hadoop) and data integration
  • Oracle Data Visualization Cloud Service – self-service data visualization
  • Oracle Big Data Preparation Cloud Service – self-service data preparation
  • Oracle Big Data Discovery Cloud Service – data science with data preparation and visualization
  • Oracle GoldenGate Cloud Service – data replication and streaming data
  • Oracle NoSQL Database Cloud Service – key value store.

Oracle has also announced Oracle Essbase Cloud Service for multidimensional analysis and Oracle Big Data SQL Cloud Service for SQL on Hadoop and NoSQL. Oracle’s Big Data Compute Edition will allow organizations to scale Hadoop compute nodes and data nodes independently. All of these are indicated on the Oracle website as “coming soon.”

Faced with such a broad portfolio of big data and analytics capabilities, it may be a challenge for potential customers to understand the portfolio and decide which pieces are required for their organization. Fortunately, services based in the cloud are easier to try since no installation is required and subscription-based licensing doesn’t require long-term commitments to products.

Part of Oracle’s value proposition, based on its long devotion to the old model of on-premises licensing and management, is a mixture of cloud and on-premises deployments, often referred to as hybrid cloud. Oracle’s cloud services are available in three vr_dac_24_data_integration_between_systemsconfigurations: as a public cloud service subscription; as a managed private cloud service subscription managed by Oracle in the customer’s data center; and licensed as an on-premises deployment managed by the customer. Oracle is betting that this flexibility with be attractive to enterprises as they make their journey to the cloud. Amazon, the key cloud competitor highlighted in Oracle’s keynotes, does not offer on-premises or hybrid configurations. Our research finds that nearly half (47%) of organizations support integration of cloud-based data with on-premises data and 38 percent vice versa, suggesting a significant presence of hybrid deployments.

We should note that most of Oracle’s big data and analytic cloud services are not new. In fact, most of Oracle’s portfolio competes with other products that have been in the market for years. Its strength is to excel at making products enterprise-ready. Others may find new and innovative ways to tackle computing challenges, but as these innovations take root in the market, Oracle adopts them, hardens them and makes them available for critical applications. It also adds innovations around the edges, but fundamentally Oracle makes these capabilities industrial-strength for dealing with issues such as security, reliability, manageability and governance – necessities that are often overlooked as new products come to market. If your organization needs to support mission-critical big data and analytics, I recommend you consider Oracle’s offerings. They have the breadth and depth to meet most needs.

Regards,

David Menninger

SVP & Research Director

Follow Me on Twitter @dmenningerVR and Connect with me on LinkedIn.

It’s part of my job to cover the ecosystem of Hadoop, the open source big data technology, but sometimes it makes my head spin. If this is not your primary job, how can you possibly keep up? I hope that a discussion of what I’ve found to be most important will help those who don’t have the time and energy to devote to this wide-ranging topic.

I was a little late to the party. I first wrote about Hadoop for Ventana Research in 2010. Apache Hadoop then was about four years old and consisted of three modules with three top-level projects and a few subprojects. It didn’t reach the version 1.0 designation until a year later, in December 2011. Since then it has continued to evolve at a pace that is always steady and sometimes dizzying. Today the Apache Foundation lists four modules and 11 projects on its Hadoop page and a total of 35 projects that fall into the big data category.

The open source model has had a major impact on the big data market, yet in some ways, the open source approach has succeeded despite its shortcomings. For one thing, it is not an ideal business model. Few “pure” open source companies have been able to make a profit. Red Hat is the most notable financial success in the open source world. Hortonworks, one of the Hadoop distribution vendors, strives to be entirely open source but has struggled to make a profit.

Instead, when it comes to commercializing open source technologies, most vendors use a hybrid licensing model that combines open source components with licensed products to create revenue opportunities. So far, this model hasn’t proven to be financially viable either. Cloudera and MapR have chosen a hybrid Hadoop model, but they are private companies that don’t disclose their financials publicly. By some analysts’ estimates Cloudera won’t be profitable until 2018, and MapR has indicated it won’t have a positive cash flow until mid-2017.

The real, if nonmonetary, value of an open source model is that it helps create a large community, one that few organizations could create on their own. Here the Hadoop community is an outstanding example. The Strata+Hadoop World events will take place in five different locations this year, and organizers expect to attract a combined audience of more than 10,000 attendees. The Hadoop Summits will take place in four different cities and also attract thousands of attendees. On the adoption front, nearly half (48%) of the participants in our big data integration benchmark research said they now use Hadoop or plan to use it within 12 months.

A large community such as this one typically spawns more innovation than a small community. This is both the blessing and the curse of the Hadoop ecosystem.

Hadoop constantly changes. New projects are created as the community seeks to improve or extend the existing capabilities. For example, in many cases, the MapReduce programming model is being supplemented or replaced by Spark, as I have noted. In its original incarnation, Hadoop was primarily a batch-oriented system, but as it grew in popularity users started to apply it in real-time scenarios including Internet of Things (IoT) applications, which I’ve written about. Multiple Apache projects sprung up to deal with streaming data including Flink, Kafka, NiFi, Spark Streaming and Storm.

Regarding the last capability, all the major Hadoop distribution vendors have adopted some form of streaming data. Cloudera uses Spark and is adding Envelope and Kudu for low-latency workloads. Earlier this year, Hortonworks launched its second product, Hortonworks Data Flow, which is based on Kakfa, NiFi and Storm for streaming data. MapR introduced MapR Streams to deal with streaming data and IoT applications using the Kafka API. It’s clear that Hadoop vendors see a need to provide streaming of data, but the variety of approaches creates confusion for organizations about which approach to use.

Early Hadoop distributions did not emphasize security and governance. In our research more than half (56%) of organizations said they do not plan to deploy big data integration capabilities because it poses security risks or issues. Now those gaps are being addressed. The Apache Knox, Ranger and Sentry projects add security capabilities to Hadoop distributions. Unfortunately, there is not much consistency among vendors on which of these projects they support, again creating more confusion about which projects to use. Two other Apache projects, Atlas and Flacon, are designed to support data governance capabilities. Atlas and Ranger are still in the incubation process, the Apache process for accepting new products, but nothing prevents vendors from adopting these projects at this stage.

So how should your organization deal withvr_BDI_02_state_of_big_data_technology all these moving parts? Here’s my recipe. First it is important to have the skilled resources needed to manage big data projects. In our research 44 percent reported that they don’t have the Hadoop-specific skills needed. Those without them should consider hiring or contracting appropriately skilled Hadoop resources. However, some vendors provide packaged Hadoop offerings that reduce the need to have all the skills in house. For instance, there are cloud-based versions of Cloudera, Hortonworks and MapR. Amazon EMR also provides a managed Hadoop framework. Some vendors recognized the shortage of skills and have built businesses around offering big data as a service including Altiscale and BlueData.

Analytic database and data warehouse vendors have also attempted to make it easier to access and take advantage of Hadoop. These products typically take the form of SQL capabilities on Hadoop, an appliance configuration that comes installed with Hadoop or a cloud-based service that includes Hadoop. This table summarizes several vendors’ offerings.

Hadoop_Ecosystem_Menninger

The Open Data Platform initiative (ODPi), an industry consortium, attempts to reduce the skills needed to master different projects and versions within the Hadoop ecosystem by defining specifications for a common set of core Hadoop components. Currently Hortonworks and IBM offer ODPi-compliant versions of their Hadoop distributions, but Cloudera and MapR do not. The specification provides value to those who are looking for stable versions of the core Hadoop components.

The SQL on Hadoop products mentioned above still require that an organization have Hadoop, but it is worth considering whether you need Hadoop at all. Snowflake Computing was founded on the premise that organizations want to take advantage of the SQL skills they already have. This vendor built a cloud-based elastic data warehouse service that can scale and accommodate diverse data types while retaining a SQL interface. This approach may not be far-fetched; our research shows that relational databases are still the most commonly used big data technology.

To say the least, the Hadoop ecosystem is varied and complex. The large community surrounding big data continues to produce innovations that add to the complexity. While organizations can derive significant value from Hadoop, it does require investment. As your organization considers its investments in big data, determine which approach best suits its requirements and the skills available.

Regards,

David Menninger

SVP & Research Director

Follow Me on Twitter @dmenningerVR and Connect with me on LinkedIn.

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