Mario Meir-Huber
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RACEing to agile Big Data Analytics

I am happy to announce the development we did over the last month within Teradata. We developed a light-weight process model for Big Data Analytic projects, which is called “RACE”. The model is agile and resembles the know-how of more than 25 consultants that worked in over 50 Big Data Analytic projects in the recent month. Teradata also developed CRISP-DM, the industry leading process for data mining. Now we invented a new process for agile projects that addresses the new challenges of Big Data Analytics.
Where does the ROI comes from?
This was one of the key questions we addressed when developing RACE. The economics of Big Data Discovery Analytics are different to traditional Integrated Data Warehousing economics. ROI comes from discovering insights in highly iterative projects run over very short time periods (4 to 8 weeks usually) Each meaningful insight or successful use case that can be actioned generates ROI. The total ROI is a sum of all the successful use cases. Competitive Advantage is therefore driven by the capability to produce both a high volume of insights as well as creative insights that generate a high ROI.
What is the purpose of RACE?
RACE is built to deliver a high volume of use cases, focusing on speed and efficiency of production. It fuses data science, business knowledge & creativity to produce high ROI insights
How does the process look like?

RACE - an agile process for Big Data Analytic Projects

RACE – an agile process for Big Data Analytic Projects


The process itself is divided into several short phases:

  • Roadmap.That’s an optional first step (but heavily recommended) to built a roadmap on where the customer wants to go in terms of Big Data.
  • Align. Use-cases are detailed and data is confirmed.
  • Create. Data is loaded, prepared and analyzed. Models are developed
  • Evaluate. Recommendations for the business are given

In the next couple of weeks we will publish much more on RACE, so stay tuned!

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Mario Meir-Huber
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What is necessary to achieve interoperability in the Cloud?

What is necessary to achieve interoperability in the Cloud?

As described in the previous sections, 3 major interoperability approaches arise. First, there is the standardisation approach, next there is the middleware approach and last but not least there is the API approach. This is also supported by [Hof09] and [Gov10]. In addition to that, [Gov10] suggests building abstraction layers in order to achieve interoperability and transportability.
There are two main aspects where interoperability is necessary. One level is the management level. This deals with handling the virtual machine(s), applying load balancing, setting DNS settings, auto scaling features and other tasks that come with IaaS solutions. However, this level is mainly necessary in IaaS solutions as PaaS solutions already take care of most of it. The other level is the services level. The services level is basically everything that comes with application services such as messaging, data storage and databases.
interop approaches
Figure: Cloud interoperability approaches
These requirements are described in several relevant papers such as [End10], [Mel09], [Jha09].
Parameswaran et al. describes similar challenges for Cloud interoperability. The authors see two different approaches, the first via a unified cloud interface (UCI) and the second via Enterprise Cloud Orchestration [Par09].
A unified cloud interface is basically an API that is written “around” other cloud APIs that is vendor specific. It requires some re-writing of and integration. This is similar to the approach selected by Apache jClouds and Apache libcloud.
The Enterprise Cloud Orchestration is a layer where different cloud providers register their services. The platform then provides these services for users like in a discovery. This is very similar to UDDI. The downside of this is that the orchestration layer still needs to integrate all different services (and built wrappers around them). However, it is transparent to the end-user.
Enterprise orchestration layer
Figure: Enterprise Orchestration Layer [Par09]
This post is part of a work done on Cloud interoperability. You can access the full work here and the list of references here.

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Mario Meir-Huber
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Discussion of existing standards and frameworks for Cloud Interoperability

As discussed in the previous sections, there are several standards and interoperability frameworks available. Most of them are infrastructure related. The standards and frameworks can generally be clustered into 3 groups.
The first group is the “Standards” group, which consists of OCCI an the DMTF standards. The second group is the “Middleware” group. This group contains mOSAIC, the PaaS Semantic Interoperability Framework and Frascati. The third group is the “Library” group. This group is a concrete implementation that provides a common API for several cloud platforms. The two projects in here are Apache jClouds and Apache libcloud.

Interoperability Solutions for the Cloud

Interoperability Solutions for the Cloud


Figure: Interoperability in the Cloud
OCCI provides great capabilities for infrastructure solutions, but there is nothing done for individual services. The same applies to the standards proposed by the distributed management task force.
As with the libraries and frameworks, a similar picture is drawn. Apache jClouds and Apache libcloud provide some interoperability features for infrastructure services. As for platform services, only the blob storage is available. When developers build their applications, they still run into interoperability challenges.
mOSAIC offers a large number of languages and services, however, it is necessary to build a layer on top of an existing platform. This is not a lightweight solution, as a developer has to maintain both – the developed application and the middleware solution installed on top of the provider. The developer eliminates the vendor lock-in, but runs into operational management of the mOSAIC platform. This eliminates a problem on the one side but might create a new one on the other side.
The same problem exists with the PaaS Semantic Interoperability Framework. A user has to install a software on top of the cloud platforms, which then has to be maintained by the user. The goal of platform as a service is to relieve the user from any operational management like in IaaS platforms. Frascati is also a middleware that needs to be maintained.
All of the libraries or frameworks described work for IaaS services. None of them support the PaaS paradigm and services related to it. Apache libcloud and Apache jClouds offer some very fundamental support for the storage service. However, other services such as messaging and key/value storage are not supported.
This post is part of a work done on Cloud interoperability. You can access the full work here and the list of references here.

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Mario Meir-Huber
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PaaS Semantic Interoperability Framework (PSIF)

PaaS Semantic Interoperability Framework (PSIF)

Loutas et al. defines semantic interoperability as “the ability of heterogeneous Cloud PaaS systems and their offerings to overcome the semantic incompatibilities and communicate” [Lou11]. The target of this framework is to give developers the ability to move their application(s) and data seamlessly from one provider to another. Loutas et al. propose a three-dimensional model addressing semantic interoperability for public cloud solutions [Lou11].
Fundamental PaaS Entities
The fundamental PaaS entities consist of several models: the PaaS System, the PaaS Offering, an IaaS-Offering, Software Components and an Application [Lou11].
Levels of Semantic Conflicts
Loutas et al. [Lou11] assumes that there are 3 major semantic conflicts that can be raised for PaaS offerings. The first one is an interoperability problem between the metadata definitions. This occurs when different data models describe one PaaS offering. The second problem is when the same data gets interpreted differently and the third is when different pieces of data have similar meaning. Therefore, [Lou11] uses a two-level approach for solving semantic conflicts. The first level is the Information Model that refers to differences with data and data structures/models. The other level is the data level that refers to differences in the data because of various representations.
Types of Semantics
Three different types of semantics are defined [Lou11]. The first type is the functional semantic. This is basically a representation of everything that a PaaS solution can offer. The second type, the non-functional semantic, is about elements such as pricing or Quality of Service. The third semantic is the execution semantic, which is describing runtime semantics.
This post is part of a work done on Cloud interoperability. You can access the full work here and the list of references here.

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Mario Meir-Huber
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Oracle and the cloud. Are you kidding me?

We all remember the day when the Big Boss at Oracle, Larry Elison expressed his feelings against the cloud (see here) and guess what? Oracle is now (finally) getting serious about the cloud – or at least, they try to.
I mean it is now 7 years or more that companies like Microsoft, IBM and others decided that the cloud is probably worth investing in (and, they gained a competitive advantage). Now, Oracle finally decided that they need to get into the cloud rather soon than late (even though they are already late).
I worked with Cloud Computing for 10 years now and looking at the service offers by Oracle on cloud computing, I have to admit that they are at a stage where Microsoft, IBM, Google and Amazon has been about 7 years ago. Their service offering is – bad. The way how their services are designed – sub-standard. They push the topic heavily, as they figured out that they have no other chance if they want to remain a large player. The question is, if it is already too late for them.
Anyway, I wouldn’t trust Oracle when it comes to Cloud Computing. Some sources actually state that Oracle is using nasty tricks to get their customers on the cloud (see here). If this is true, I wouldn’t recommend anyone to use the Oracle cloud.
I bet that Oracle will be an irrelevant player in the cloud – if they stay with the same approach like now.
 
Disclaimer: this is my personal opinion.

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Mario Meir-Huber
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What everyone is doing wrong about Big Data

I saw so many Big Data “initiatives” in the last month in companies. And guess what? Most of them failed either completely or simply didn’t deliver the results expected. A recent Gartner study even mentioned that only 20% of Hadoop projects are put “live”. But why do these projects fail? What is everyone doing wrong?
Whenever customers are coming to me, they “heard” of what Big Data can help them with. So they looked at 1-3 use cases and now want to have them put into production. However, this is where the problem starts: they are not aware of the fact that also Big Data needs a strategic approach. To get this right, it is necessary to understand the industry (e.g. TelCo, Banking, …) and associated opportunities. To achieve that, a Big Data roadmap has to be built. This is normally done in a couple of workshops with the business. This roadmap will then outline what projects are done in what priority and how to measure results. Therefore, we have a Business Value Framework for different industries, where possible projects are defined.
The other thing I often see is that customers come and say: so now we built a data lake. What should we do with it? We simply can’t find value in our data. This is a totally wrong approach. We often talk about the data lake, but it is not as easy as IT marketing tells us; whenever you build a data lake, you first have to think about what you want to do with it. Why should you know what you might find if you don’t really know what you are looking for? Ever tried searching “something”? If you have no strategy, it is worth nothing and you will find nothing. Therefore, a data lake makes sense, but you need to know what you want to build on top of it. Building a data lake for Big Data is like buying bricks for a house – without knowing where you gonna construct that house and without knowing what the house should finally look like. However, a data lake is necessary to provide great analytics and to run projects on top of that.

Big Data and IT Business alignment

Big Data and IT Business alignment


 
Summing it up, what is necessary for Big Data is to have a clear strategy and vision in place. If you fail to do so, you will end up like many others – being desperate about the promises that didn’t turn out to be true.
 

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Mario Meir-Huber
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Interoperability in the cloud: mOSAIC and Frascati

mOSAIC

mOSAIC is a European project supported by the European union [Dan10]. The target of the project was to build a unified Application programing interface (API) for Cloud services that is not only available in Java but also for other languages. mOSAIC is platform and language agnostic. It supports a large number of platforms for this approach. The mOSAIC framework itself is a middleware that runs on top of each cloud provider and abstracts provider specifics. The platform then exposes it’s own API to clients.
The mOSAIC project is built in a layered architecture. On the lowest level, there is the native API or protocol. This is either a ReST, SOAP, RPC or a language-specific library. On the next level, a driver API is found. This API can now be exchanged easily with different platforms such as Amazon’s S3. On top of that is an interoperability-API that allows programming language interoperability. Cloud resources can be access via the connector API. This is also the entry-point for developers, as they access specific models and resources from that API. On top of the connector API is a cloudlet that provides a cloud compliant programming methodology.

Frascati-based Multi PaaS Solution

Frascati is an infrastructure solution that runs on 11 different cloud solutions: Amazon EC2, Amazon Elastic Beanstalk, BitNami, CloudBees, Cloud Foundry, Dot-Cloud, Google App Engine, Heroku, InstaCompute, Jelastic, and OpenShift [Par12].
Frascati follows 3 core principles: An open service model, a configurable Multi-PaaS infrastructure and several infrastructure services. The open service model is an assembly of loosely coupled services based on a Service oriented Architecture. The configurable federated Multi-PaaS Infrastructure is a configurable kernel. Infrastructure services take care of the node provisioning, the deployment of the PaaS service, the deployment of the SaaS service and a federation management service. The Frascati services are installed on top of existing IaaS-services. To work with these services, it is necessary to have access to virtual machines (either Linux or Windows).
This post is part of a work done on Cloud interoperability. You can access the full work here and the list of references here.

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Mario Meir-Huber
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Interoperability libraries in the cloud: Apache jClouds and Libcloud

Apache jClouds

Apache jclouds is a framework provided by the Apache Software Foundation. The framework is written in Java and serves the purpose to provide an independent library for typical cloud operations. At present (November 2014), Apache jclouds provides 2 kinds of services: a compute service and a blob service [Apa14b]. Apache jclouds can be used from Java and Clojure. The library offers an abstraction for more than 30 cloud providers, including AWS, Azure, OpenStack and Rackspace.
Apache jclouds is primarily built for infrastructure interoperability. As for the platform layer, only blob storage is currently supported. The focus of jclouds is to support a large variety of platforms over implementing a large variety of services. The Blob storage in Apache jclouds works with the concept of Containers, Folders and Blobs. The library supports access control lists for objects. The upload of Multipart objects is also supported, which allows jclouds to handle large files. [Apa14c]

Libcloud

[Apa14d] Apache libcloud is similar to Apache jClouds. The library is developed as an abstraction to different cloud providers. Libcloud currently supports more than 30 different providers. Libcloud is available as a Python library.
Libcloud is infrastructure-focused with most of it’s implementations done for computing, DNS and Load balancing. There is also an object storage implementation available.
This post is part of a work done on Cloud interoperability. You can access the full work here and the list of references here.

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Mario Meir-Huber
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Standards in the Cloud: Open Cloud Computing Interface and DMTF

Open Cloud Computing Interface (OCCI)

The Open Grid Forum created the Open Cloud Computing Interface (OCCI). They claim to have one of the first standards in the cloud. OCCI was initially built to deliver portability and interoperability for IaaS platforms. In its initial implementation, it was used for different tasks around deployment, scaling and monitoring for virtual machines in the cloud. The library also supports common tasks for other cloud layers such as SaaS and PaaS [Ope14a].
OCCI is not a specific library that enables interoperability and portability. Hence, it is a definition of standards that can be implemented by individual platforms. The standard exists of 3 elements: the core description, the infrastructure description for the IaaS domain and a rendering description. The rendering is used to provide a REST HTTP service [Ope14b].
OCCI is implemented by a large number of cloud platforms. Major platforms such as Apache Cloudstack, OpenStack, OpenNebula and Eucalyptus implement that standard. However, large public cloud providers such as Amazon Web Services, IBM, Microsoft and Google don’t implement that standard [Ope14c].
The OCCI standard focuses on IaaS. At present, it supports compute, networking, storage, and infrastructure templates. Storage does not define blob storage, as it is about the storage of virtual machines [Met11].

Distributed Management Task Force (DMTF) initiatives

Distributed Management Task Force proposed several standards for cloud computing [Dmt09]. The key focus of the DMTF is to improve standards around IaaS solutions. As a key standard, the DMTF developed the Open Virtualization Format (OVF). However, there are no standards for APIs and platform services available.
This post is part of a work done on Cloud interoperability. You can access the full work here and the list of references here.

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Mario Meir-Huber
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Current initiatives in cloud computing interoperability

Gonidis [Gon11] defines an approach on how to enable interoperability for PaaS solutions. It is about building a standard for existing cloud platforms. However, this is a challenge given that every platform provider has it’s own proprietary API. Furthermore, services available on the one platform aren’t available on the other platform. In the following posts, existing interoperability frameworks, solutions and standards for Platform as a Service are evaluated.

Standard initiatives

Current standard initiatives are the Open Cloud Computing Interface (OCCI) and the Distributed Management Task Force (DMTF) initiatives. In the next posts, I will outline them in detail.

Libraries and Frameworks

Libraries and Frameworks for Cloud Interoperability are: Apache jClouds and Libcloud. They will be described in the following posts.

Middleware solutions

Middleware Solutions for Cloud interoperability are: mOSAIC, PaaS Semantic Interoperability Framework (PSIF), Frascati-based Multi PaaS Solution and SimpleCloud. They will be described in the following posts.

Interoperability Solutions for the Cloud

Interoperability Solutions for the Cloud


This post is part of a work done on Cloud interoperability. You can access the full work here and the list of references here.

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