Hadoop Platforms Dominating The Market

“As a developer, understanding the Hadoop ecosystem can make you very valuable. Companies are leveraging it for more projects each day.” – Thomas Henson: Senior Software Engineer, Certified ScrumMaster & Technical Author at Pluralsight.

Over the past decade, the world has seen the launch of a multitude of ambitious frameworks and solutions aimed at tackling all your Big Data challenges, and we found which of the hadoop-integrated platforms were dominating the market.

 

#1 – CLOUDERA ENTERPRISE

The only platform with the native Hadoop Search engine. With high performance, low cost and advanced optimisation features, Cloudera Enterprise is among the most popular choices for enterprises who want fast, easy and secure Big Data projects.

“Cloudera supplements everything we liked about Hadoop by providing a clear path to being production-ready, ease of management, and top performance. We now have the agility to quickly react to new situations and deliver market-leading capabilities to our clients.” – CounterTack.

 

#2 – APACHE SPARK

Internet powerhouses like Netflix, Yahoo and eBay are loving Apache Spark, having deployed it at massive scale. Known for its speed, ease of use and generality, Apache Spark supports Scala, Python and Java and boasts speed 100-times faster than Hadoop for large-scale data processing.

“Spark … is what you might call a Swiss Army knife of Big Data analytics tools.” – Reynold Xin: Berkeley AmpLab Shark Development Lead.

 

#3 – APACHE HIVE

“Hive is the closest thing to a relational-database in the Hadoop ecosystem.” – Pluralsight.

Hive is data warehousing framework that allows for structuring and querying data using a language called HiveQL to write complex MapReduce over structured data in a distributed file system. Companies like Facebook, Qubole Inc. and Tata Consultancy Services are using this software to read, write and manage large datasets.

 

#4 – APACHE PIG

With big-time users like LinkedIn, Twitter and Salesforce, Apache Pig is popular for its ease of programming and customisation capabilities. It transforms large data sets with its own SQL-Like language. While applications like Hive are used for structured data, Pig is famous for transforming semi-structured and unstructured data, allowing developers to write complex MapReduce jobs without having to write them in Java.

 

#5 – APACHE SQOOP

“Not just a good general-purpose tool, but also a high-performance solution.” – Justin Kestelyn: Group Product Marketing Manager, Google Cloud Platform- Data Processing & Analytics.
Highly utilised for its efficiency and convenience, Sqoop allows developers to transfer data from a relational database into Hadoop, with significant opportunities for optimisation. Bundled with various connectors, it can be used for popular database and data warehousing systems such as such as Teradata, Netezza, Oracle, MySQL, Postgres, and HSQLDB.

 

#6 – APACHE ZOOKEEPER

The ultimate enabler of highly reliable distributed coordination in the Hadoop Ecosystem. ZooKeeper provides centralised services for configuration, synchronisation and group services.

“We use ZooKeeper extensively for discovery, resource allocation, leader election and high priority notifications. Our entire service is built up of multiple systems reading and writing to ZooKeeper.” – Konrad Beiske: Software Engineer at Elastic.co.

For more resources, please see the links below:

 

The Hadoop Ecosystem:

Big Data: 5 Major Advantages of Hadoop

Hadoop: The Ultimate List of Frameworks

20 Essential Hadoop Tools For Crunching Big Data

 

Cloudera Enterprise:

Cloudera Enterprise – Data Sheet

 

Apache Spark:

What Is Apache Spark?

Why All This Interest In Spark?

 

Apache Pig:

Apache Pig Overview

8 Reasons Why You Should Be Using Apache Pig

 

Apache Zookeeper:

Apache Zookeeper – The King of Coordination

 

Apache Sqoop:

Apache Sqoop – Hortonworks

Sqoop – The Apache Software Foundation!

July Insights On Big Data & Business Transformation

It’s been a busy month in Big Data, and we’ve been deep in learning about what factors are driving the industry. We’ve translated our experience, research and insight into five key lessons.

Here’s what we’ve learnt this month.

 

#1 – PLAN & TEST FIRST, INTEGRATE LATER

“Big data has proven to be a valuable business asset, but using it to gain competitive advantage requires the right combination of strategy, technology and execution.” – Mahmood Majeed: Managing Partner at ZS Associates.

In all the excitement of implementing a Big Data strategy, It’s easy to get caught up in the hype of new technologies promising to do magical things with your data, and make hasty decisions that once integrated, can be disappointing.

To avoid this, team leaders of Big Data projects must ensure that expectations and output are aligned.

“Start by developing a strategy across the entire enterprise that includes a clear understanding of what you hope to accomplish and how success will be measured.” – Harvard Business Review.

 

#2 – SECURITY IS NOT AN AFTERTHOUGHT, IT’S A NECESSITY

“Big Data and analytics is showing promise with improving cyber security. 90% of respondents from MeriTalk’s new U.S. government survey said they’ve seen a decline in security breaches.” – SentinelOne.

With the volume of worldwide data reaching unprecedented levels, new cyber security threats are emerging daily. To combat this, an article in CSO discusses the benefits of using historical data to identify potential cyber attacks while also predicting future events.

“Using this historical data, you can create statistical baselines to identify what is ‘normal’. You will then be able to determine when the data deviates from the norm.This historical data can also create new possibilities for predictive models, statistical models, and machine learning.”

 

#3 – IT’S ABOUT QUALITY, NOT VOLUME

“Because Big Data presents new features, its data quality also faces many challenges.” – Li Cai & Yangyong Zhu: Fudan University.

As data evolves, new challenges emerge which is why it’s important for businesses to develop data quality standards. With the rise of insight-driven business models, the quality of the data used is key to making the right decisions. Leaders of Big Data projects must ensure that the data is accurate for the intended use.

“Data quality depends not only on its own features, but also on the business environment using the data, including business processes and business users.” – Data Science Journal.

 

#4 – BIG DATA + AGILE = SUCCESS

“The longer you take to find the data, the less valuable it becomes.” – Wired.

With recent advancements in technology, more emphasis is being placed on data agility and the importance of data-driven insights in real-time. “How fast can you extract value from your mountains of data and how quickly can you translate that information into action?” – tdwi.org.

Ian Abramson, former Director at EPAM Systems, describes the alignment of Big Data and Agile as “the infrastructure and framework to be successful.” He then goes on to talk about how this alignment enables focus and a clear picture of how to get from A to B. “ What is the question, what is the success factor, how will we get there, and who will be involved?”

At the 2016 International Conference on Ambient Systems, Networks and Technologies (ANT), experts in the field of Big Data were asked, “ What do you think is most important in the management of Big Data projects?” Interestingly, respondents rated three out of the Four Values of The Agile Manifesto as the highest in their answers, which were: cooperation with customers, people & communication, and working with software over comprehensive documentation.

 

#5 – SKILL-UP OR MISS OUT

“Is the world going to become a place in which automation is everywhere yet employment is scarce?” – Michael Grothaus: The Hanbury Literary Agency.

According to KPMG’s 2016 CIO Survey, data analytics is the most in-demand technology skill for the second year running, but nearly 40% of IT leaders say they suffer from shortfalls in skills in this critical area. “Big Data training is beneficial in meeting the demands of Data Management, faster decision making, better understanding of customers and tapping into the right demographic.” – Vikrant Singh: Senior Manager at Xebia Group International.

In a recent article , Sophia Bernazzani, Marketing Manager at HubSpot says, “The fact remains that some jobs will be replaced by machines – it’s the essence of any industrial or technological revolution. The good news is; some jobs won’t be strictly replaced – they just might be adjusted to account for new technologies.”

So how do we skill-up and adopt these new technologies into our businesses? “By training those already with the company, businesses get to keep valuable team members that already have experience with the enterprise while giving them some much-needed skills.” – Rick Delgado: Enterprise Tech Commentator & Writer.

“Training employees can never be a liability. It is in fact an asset, an investment to the company.” – Big Data Trunk.

 

For more resources, please see below:

 

Data Integration

How To Integrate Data And Analytics Into Every Part of Your Organisation

Charting An Effective Big Data Strategy

Big Data: The Management Revolution

 

Data Quality

Beyond Volume, Variety & Velocity Is The Issue of Big Data Veracity

The Challenges of Data Quality Assessment In The Big Data Era

Big & Fast Data: The Rise of Insight-Driven Businesses

 

Cyber Security

How Big Data Is Improving Cyber Security

Big Data Security Analytics: A Weapon Against Cyber Security Attacks

 

Data Skills

The Importance of Employee Training

Businesses And The Big Data Skills Shortage

Big Data Jobs Are Out There: Are you Ready?

The Importance of Big Data Training to Your Data Analysis Growth

10 Jobs Artificial Intelligence Will Replace (and 10 That Are Safe)

 

Data Agility

Big Data and Agile: The Perfect Marriage

Agile Project Management And Its Use in Big Data Management

How To Succeed On Your Big Data Journey

“The world has become excited about Big Data and advanced analytics, not just because the data are big but also because the potential for impact is big.” – David Court: Director at McKinsey & Company.

Big Data Analytics is not just a project. It’s a journey, and there are steps you can take to improve your chances of success.

 

#1 – ADAPTING TO CHANGE

“How do you pick the framework that is here to stay? You don’t—because you can’t.” – Syncsort.

With rapidly evolving tools and frameworks, a challenge for businesses is to invest in applications that won’t need to be replaced in 12 months. Many are turning to Apache Hadoop for its speed and efficiency, but in an industry where change is the only constant, future-proofing Big Data Software has become a major investment for businesses.

“A recent Robert Half Management Resources Survey found that 41% of CFOs believe staying current with changing technology is the greatest pressure their accounting and finance teams face.” – Mark Sands: General Manager, Asia Pacific for BOARD International.

 

#2 – RESEARCH

“Part of efficient Big Data Analytics is selecting the right platform to help you through it. But what should you look for? And do you want to build your solution, buy it, or bridge an available software with what you have in-house?” – Sherry Tiao: Content Marketing Manager at Datameer.

This is where research comes into play, and what tools and technologies you decide to integrate rely fundamentally on what problem you’re trying to solve. One of the key factors to consider is what your data platform drivers are – storage or advanced analytics?

“For organisations needing to store and process tens of terabytes of data, using an open-source distributed file system is a mature choice due to its predictable scalability over clustered hardware. However, if you’re looking to run analytics in online or real-time applications, consider hybrid architectures containing distributed file systems combined with distributed database management systems.” – Nick Millman: Data & Analytics Leader for Accenture.

 

#3 – AD-HOC EXPERIMENTATION

“The earliest phase, where organisations experiment with and learn about their Big Data needs.” – Datameer.

This is is the initial step where the team is trying to understand what data can be analysed, who can analyse it, brainstorm ideas and identify challenges in a cost-effective and timely way.

“Typical problems encountered during the stage include missing or ill-prepared data, and the reliance on manual labor for data processing.” – Chris Raphael: Former Editorial Director & Content Strategist at RT Insights.

Experimentation is crucial for identifying problems early. It’s better to fail fast and fail cheap than to invest in the wrong platform and face disappointed customers.

“Fail often – obviously, try lots of things. As you discover what is working, do more of it. And what does not work gets cut and is not a failure, it is a learning of what does not work.” – Canrock Ventures.

 

#4 – THE RIGHT USE-CASE

“You need to know how, and why, Big Data is useful to your company.” – Talend.

What problem are you trying to solve, and what should you consider when looking for a Big Data solution? choosing the right use-case can be the difference between the success or failure of your Big Data project. Although it can be tempting to try and tackle the biggest and most complex business problem as soon as you’ve been given the go-ahead to implement a Big Data platform, the best approach is to start small.

“Go small. Very small. For example, starting with one low-key business problem and a few easily accessible datasets. If you don’t, you could unknowingly be winding down the path to failure.” – Ben Sharma: Co-Founder & CEO of Zaloni.

 

#5 – DATA GOVERNANCE

“At its core, data governance is about data trust and accountability, married with comprehensive data security best practices.” – Rob Marvin: Assistant Editor of PCMag.

A good data governance plan consists of a data management strategy, ongoing monitoring of data quality and selective access. “What’s the data you have, who has access to it, and how are you managing the lineage of that data over time?” Jack Norris: Senior VP of Data & Applications at MapR.

Data governance is not only used to manage risk, but also to make sure that there are as few errors as possible.“Through a proper process, companies can implement the appropriate data governance initiatives and framework, which creates structure and accountability to data.” – Desire Athow: Editor at TechRadar.

 

For more resources, please see below:

 

Big Data Projects

Five Phases of Big Data Projects

Getting Big Impact From Big Data

Big Data: Changing The Way Businesses Operate

11 Tips For Ensuring Your Big Data Initiative Succeeds

Five Big Data Challenges (Plus Free Resources To Help)

How To Turn Any Big Data Project Into A Success (And Key Pitfalls To Avoid)

 

Adapting To Change

8 Considerations When Selecting Big Data Technology

As a CFO, How Do You Keep Up With All The Technology Changes?

Keeping Up With Big Data Innovation Without Disrupting Your Business

 

Big Data Use Cases

Your First Big Data Success: Choosing The Right Use Case

How Is Big Data Used In Practice? 10 Use Cases Everyone Must Read

 

Data Governance

The Growing Importance Of Data Governance

Big Data Basics: How To Build A Data Governance Plan

How To Avoid Costly Data Errors In The Enterprise

Hottest Trends Driving Big Data In 2017

How businesses are using data is evolving, and the rise of new technology is changing data-driven projects for the better.

Here’s some of the hottest trends we’ve observed this year.

 

#1 – THE RISE OF CLOUD

“Enterprise IT had been rapidly changing, and the Cloud is playing an ever-larger factor. Cloud technologies offer unprecedented resource and flexibility for Big Data & Data Science.” – Zmags.

With the growing popularity of cloud computing, there’s been a lot of debate about the pros and cons of moving data from on-premise to cloud environments.

“Contrary to popular belief, on-premise systems were shown to be more expensive to operate annually than their cloud-based counterparts, with the overall cost 60% higher on an annual basis – even after the first year!” – Andrew Heriot: Head of Services EMEA at Maximizer Software.

With many businesses moving to cloud solutions as a way of cutting down costs, other key reasons why they’re deciding to make the switch are simplicity, flexibility, accessibility and experimentation.

“The cloud enables new kinds of possibilities, including inexpensive experimentation that allows businesses to configure ‘best fit’ solutions that satisfy their needs.” – Vasant Dhar: Professor at the Stern School of Business & the Centre for Data Science at New York University.

 

#2 – CYBER SECURITY

“The world will create 180 zettabytes of data (or 180 trillion gigabytes) in 2025, up from less than 10 zettabytes in 2015, according to IDC.” – Forbes.

With worldwide data reaching unprecedented levels, cyber attacks have also become more prevalent. As a result, cyber security through Big Data Analytics has become a major area of investment, and businesses who treat it as an afterthought rather than a major area of concern are leaving themselves vulnerable.

“An ASX survey of the cyber risk facing Australia’s top 100 publicly listed companies found that nearly two-thirds of Australian companies see cyber breaches as an “IT issue” rather than a major business risk, and only 45 per cent of ASX 100 companies are confident in their organisation’s ability to detect and manage a cyber hacking event.” – Alice Uribe: Australian Financial Review.

 

#3 – DATA AGILITY

“It’s not about how much data you can store and process. It’s about data agility. How fast can you extract value from your mountains of data and how quickly can you translate that information into action?” – tdwi.org

Businesses have shifted their focus from capturing and managing data to actively using it for business impact. To achieve this, new tools to discover and explore data are being developed for greater flexibility and speed. A great example of this would be Cloudera Altus, which our partner released earlier this year, taking the deployment of data platforms and data pipelines in the cloud to the next level.

“Our customers wanted to understand how to leverage the agility, scale, and ease-of-use offered by the cloud to efficiently and cost-effectively gain insights from their ever-growing business data.” Jennifer Wu: Director of Product Management at Cloudera.

 

#4 – DATA VISUALISATION

“Having the data is not enough, I have to show it in ways people both enjoy and understand.” – Hans Rosling (1948-2017): Professor of International Health at Karolinska Institutet & Co-Founder of the Gapminder Foundation.

With technology advancing at a rapid pace, data visualisation is constantly evolving. A recent Business2community survey of data professionals found that the data science skill with the highest correlation to project success was data mining and visualisation tools such as Tableau and JavaScript.

“Data visualisation is the best way of engaging decision makers with a visual narrative that leads them to the insight. It also shows the quality of the data, where data is missing, and whether it’s valid with a quick, preliminary visualisation.” – Alex Lane: International Events Coordinator at Innovation Enterprise.

 

#5 – FROM IOT TO IOE

Forget the Internet of Things (IoT). The Internet of Everything (IoE) is re-inventing how we do business by bringing together people, process, data and things to create new capabilities, experiences and opportunities for businesses by connecting them to more valuable networks.

“The Internet of Everything builds on the foundation of the Internet of Things by adding network intelligence that allows convergence, orchestration and visibility across previously disparate systems.” – Cisco.

With digital technology constantly improving products and services, customer expectations are raised and businesses must rise up to meet the challenge. Now that digital is embedded in everything we do, IoT is not enough.

“The Internet of Everything will re-invent industries at three levels: business process, business model, and business moment.” – Hung Le Hong: Research Vice President and Gartner Fellow.

 

#6 – THE STARTUP BOOM

“Big Data has become a crucial growth enabler by empowering companies with deep insights on the internal business processes along with the competitors and market. This exponential demand for data has led to the mushrooming of startups focusing on acquiring, analysing and building innovative products on top of Big Data.”- Jacob Koshy: Content & Social Media Marketer at PromptCloud.

Big Data can achieve big outcomes, but is useless without the skills to analyse it. Recognising the challenge, several startups have emerged with their own solutions to making sense of this data and turning it into actionable insights for businesses.

“When a new startup comes up with technological advancement in Big Data and machine learning, none of the big guys want their competitors getting hold of it. This along with the advantage of having the best tools to handle data makes acquiring such startups a lucrative thing to do for the bigger companies.” – PromptCloud.

Companies like Apple, Microsoft and SAP are acquiring small start-ups for competitive advantage when entering into new markets, creating new business models, and making their enterprises more customer-centric.

 

For more resources, please read the following links:

 

Big Data Trends

Top 10 Big Data Trends 2017

5 Trends Driving Big Data in 2017

Big Data Trends To Watch Out for In 2017

15 Data and Analytics Trends That Will Dominate 2017

6 Predictions For The $203 Billion Big Data Analytics Market

 

Cloud

Five Ways to Move Your Big Data Projects Into the Cloud

The Cloud or Not to Cloud: Where Does Your Data Warehouse Belong?

Industry Experts Discuss Advantages & Risks of Shifting Data Analytics to The Cloud

 

Data Agility

Ready, Set, Go – How Fast Is Your Data?

Why Data Agility is a Key Driver of Big Data Technology Development

 

Cyber Security

How Big Data is Improving Cyber Security

Commonwealth Bank of Australia Years Ahead of Rivals on Cyber Security

 

The Internet of Everything

Internet of Everything FAQ

The Internet of Everything (IoE)

 

Big Data Startups

Why Large Enterprises are Acquiring Big Data Startups

Big Data at Work: Key Lessons from Startups and Large Firms

Five Lessons In June 2017 On Big Data Success

At Contexti, we’re always striving to learn from our own experiences and from the insights of other industry leaders.

Here are five lessons we noted from our industry peers this month:

 

#1 – IT TAKES MANY HANDS, SKILLS & PERSONALITIES

Launching Big Data projects & making data-driven decisions requires a team with a variety of technical, business and soft skills. When working on projects, it’s important to have different voices and skills at the table. “Marketing and data teams should move closer together and explain in simple terms the likely outcomes of the insights created,” – Sherine Yap: global head of CRM at Shell.

 

#2 – SHARE THE VISION, THE JOURNEY, THE PITFALLS & THE SUCCESSES

In Chapter 1 of ‘Learning to Love Data Science’ by Mark Barlow, he shares his insight on communication, a fundamental part of any project.

“After you’ve laid out a roadmap of the project so everyone knows where they are going, you need to provide them with regular updates. You need to communicate. If you stumble, you need to let them know why you stumbled and what you will do to overcome the barriers you are facing. Remember, there’s no clear path for Big Data projects. It’s like Star Trek – you’re going where no one has gone before.”

 

#3 – PLATFORMS, TOOLS & DATA STRUCTURES MATTER

‘Every organization seeking to make sense of big data must determine which platforms and tools, in the sea of available options, will help them to meet their business goals.’ – Nick Millman: Data & Analytics Leader for Accenture.

Nick Millman goes on to discuss the importance of the structure of data.

‘How applications consume data should also be taken into consideration. For instance, some existing tools allow users to project different structures across the data store, giving flexibility to store data in one way and access it in another. Yes, being flexible in how data is presented to consuming applications is a benefit, but the performance may not be good enough for high velocity data. To overcome this performance challenge, you may need to integrate with a more structured data store further downstream in your data architecture.’ – Computerworld (from IDG)

 

#4 – IT TAKES TIME TO FIND THE GEMS

“What’s really important about Big Data is to understand that there’s a lot of this data, most of it’s completely worthless to the business, but there are these gems, these nuggets of information, like the fact a customer just had a baby. You want to take that information, you want to integrate it to your business decisions and make more money for your company.” – Andy Mendelsohn: Senior VP of Database Server Technologies at Oracle.

 

#5 – LEARNING FROM FAILURE

Sample, test and learn – should be the nature of your Big Data project.

“You can only fail better only if you learn from failures. And then failing is something that prompts you to move ahead.” – Pearl Zhu, Digital Agility: The Rocky Road from Doing Agile to Being Agile.

 

For more resources, please see the links below:

Google Books – Learning to Love Data Science by Mark Barlow (O’Reilly Media)

Marketing Magic Meets Big Data: How To Make Technology and Creativity Work Together

8 Considerations When Selecting Big Data Technology

An Introduction to Big Data – Smart Insights Digital Marketing Advice