Moving your data warehouse to the cloud: Look before you jump

By Ravi Dharnikota

Where’s your data warehouse? Is it still on-premises? If so, you’re not alone. Way back in 2011, in its IT predictions for 2012 and beyond, Gartner said, “At year-end 2016, more than 50 percent of Global 1000 companies will have stored customer-sensitive data in the public cloud.”

While it’s hard to find an exact statistic on how many enterprise data warehouses have migrated, cloud warehousing is increasingly popular as companies struggle with growing data volumes, service-level expectations, and the need to integrate structured warehouse data with unstructured data in a data lake.

Cloud data warehousing provides many benefits but getting there isn’t easy. Migrating an existing data warehouse to the cloud is a complex process of moving schema, data, and ETL. The complexity increases when restructuring of database schema or rebuilding of data pipelines is needed.

This post is the first in a “look before you leap” three-part series on how to jump-start your migration of an existing data warehouse to the cloud. As part of that, I’ll also cover how cloud-based data integration solutions can significantly speed your time to value.

Beyond basic: The benefits of cloud data warehousing

Cloud data warehousing is a Data Warehouse as a Service (DWaaS) approach that simplifies time-consuming and costly management, administration, and tuning activities that are typical of on-premises data warehouses. But beyond the obvious – data warehouses being stored in the cloud - there’s more. Processing is also cloud-based, and all major solution providers charge separately for storage and compute resources, both of which are highly scalable.

All of which leads us to a more detailed list of key advantages:

  • Scale up (and down): The volume of data in a warehouse typically grows at a steady pace as time passes and history is collected. Sudden upticks in data volume occur with events such as mergers and acquisitions, and when new subjects are added. The inherent scalability of a cloud data warehouse allows you to adapt to growth, adding resources incrementally (via automated or manual processes) as data and workload increase. The elasticity of cloud resources allows the data warehouse to quickly expand and contract data and processing capacity as needed, with no impact to infrastructure availability, stability, performance, and security.
  • Scale out: Adding more concurrent users requires the cloud data warehouse to scale out. You will be able to add more resources – either more nodes to an existing cluster or an entirely new cluster, depending on the situation – as the number of concurrent users rises, allowing more users to access the same data without query performance degradation.
  • Managed infrastructure: Eliminating the overhead of data center management and operations for the data warehouse frees up resources to focus where value is produced: using the data warehouse to deliver information and insight.
  • Cost savings: On-premises data centers themselves are extremely expensive to build and operate, requiring staff, servers, and hardware, networking, floor space, power, and cooling. (This comparison site provides hard dollar data on many data center elements.) When your data warehouse lives in the cloud, the operating expense in each of these areas is eliminated or substantially reduced.
  • Simplicity: Cloud data warehouse resources can be accessed through a browser and activated with a payment card. Fast self-service removes IT middlemen and democratizes access to enterprise data.

In my next post, I’ll do a quick review of additional benefits and then dive into data migration. If you’d like to read all the details about the benefits, techniques, and challenges of migrating your data warehouse to cloud, download the Eckerson Group white paper, “Jump-Start Your Cloud Data Warehouse: Meeting the Challenges of Migrating to the Cloud.

Ravi Dharnikota is Chief Enterprise Architect at SnapLogic. Follow him on Twitter @rdharn1

The commoditization of integration

By Dinesh Chandrasekhar

Eight years ago, dozens of integration vendors were offering scores of solutions, all with what seemed to be the same capabilities. Pick any ESB or ETL tool and each seemed to perform the same functions as their competitors. RFPs were no longer a viable way to weed out the inferior vendors as each solution checked all the boxes across the board. Plus, all vendors were ready to lower their prices at the drop of a hat to win your business. It was at this time that the integration market had truly reached a level of commoditization. Consumers could easily pick and choose any solution as there were no true differentiators amongst them.

But, several factors have changed the landscape since then:

  • NoESB – The NoESB architecture had started gaining interest – pushing the idea of the irrelevancy of ESB for many integration scenarios. Yet, an API Gateway was not the right alternative.
  • Cloudification – The cloudification of pretty much all your favorite on-premises enterprise applications began around the same time. Enterprises that were thinking of a digital transformation couldn’t get too far without a definitive cloud strategy in place.
  • Convergence of ESB and ETL – The lines between application integration and data integration were blurring. CIOs and IT managers didn’t want to deal with two different sets of integration tools. With the onset of mobile and IoT, data volumes were exploding daily. As a result, even data warehouses moved to the cloud. To serve such big data needs, the traditional/legacy ESB/ETL tools were incompetent and unfit.
  • Agile Integrations – Finally, the DevOps and Agile movements impacted enterprise integration initiatives as well. They had given rise to new user personas in the enterprise – Citizen Integrators or Citizen Developers. These are the LOB Managers or non-IT personnel that needed quick integrations within their applications to render their data in different views. The reliance on IT to deliver solutions to business was becoming a major hindrance.

All these factors have influenced the iPaaS (Integration Platform as a Service) market. Now, thousands of companies are already leveraging iPaaS solutions to integrate their cloud and on-premises solutions. iPaaS solutions break away from legacy approaches to integration, are cloud-native, intuitive, fast, self-starting, support hybrid architectures, and offer connectors to a wide range of on-premises and on the cloud applications.

Now comes the big question – “Will iPaaS solutions be commoditized, too?” At the moment, the answer is a definite NO and there are multiple reasons why. Beyond scale, latency, tenancy, SLAs, number of connectors etc., one of the key areas that will differentiate iPaaS solutions is the developer experience. The user interface of the solution will determine the adoption rate and the value it brings to the enterprise. So, for a citizen integrator to actually use the system, the interface should be intuitive enough to guide them in building their integration flows quickly, effectively, and most importantly, without the assistance of IT. This alone will make or break the system adoption.

iPaaS vendors are trying to enhance this developer experience with features like drag-and-drop connectors, pipeline snippets, a templates library, a starter kit, mapping enhancements, etc. However, very few vendors are offering AI-driven tooling that enables intelligent ways to predict next steps – based on learnings from hundreds of other users – for your integration flow. AI-assist is truly a great benefit for citizen integrators, who may be non-technical. Even technically savvy developers welcome a significant boost in their productivity. With innovations like this happening, the iPaaS space is quite far away from being commoditized. However, enterprises still need to be wary of cloud-washing iPaaS vendors that offer “1000+” connectors, a thick-client IDE, or an ESB wrapped in a cloud blanket. And, that is a post for a different day!

Dinesh Chandrasekhar is Director of Product Marketing at SnapLogic. Follow him on Twitter @AppInt4All.

Mossberg out. Enterprise technology still in

By Gaurav Dhillon

A few weeks ago, the legendary tech journalist, Walt Mossberg, penned his last column. Although tech journalism today is vastly different than it was in 1991, when his first column appeared in the Wall Street Journal, or even five or 10 years ago, voices like Walt’s still matter. They matter because history matters – despite what I see as today’s widely held, yet unspoken belief that nothing much important existed prior to the invention of the iPhone.

Unpacking that further, history matters because the people who learn from it, and take their cues from it, are those who will drive the future.

Enterprise tech history is still unfolding

I like to think of myself as one of those people, certainly one who believes that all history is meaningful, including tech history. As tech journalism’s eminence grise, Walt not only chronicled the industry’s history, he also helped to define it. He was at the helm of a loose cadre of tech journalists and industry pundits, from Robert X. Cringely to Esther Dyson, who could make or break a company with just a few paragraphs.

Walt is now retiring. So what can we learn from him? The premise of his farewell column in Recode is that tech is disappearing, in a good way.”[Personal] tech was once always in your way. Soon, it will be almost invisible,” he wrote, and further, “The big software revolutions, like cloud computing, search engines, and social networks are also still growing and improving, but have become largely established.”

I’ll disagree with Walt on the second point. The cloud computing revolution, which is changing the way enterprises think and operate, is just beginning. We are at a juncture populated by unimaginably large quantities of data, coupled with an equally unquenchable thirst by enterprises to learn from it. The world has gone mad for artificial intelligence (AI) and analytics, every permutation of which is fueled by one thing: data.

The way we use data will become invisible

In his column, Walt observed that personal tech is now almost invisible. We use and benefit from it in an almost passive way. The way data scientists and business users consume data is anything but. Data is still moved around and manually integrated, on-premises and in the cloud, with processes that haven’t changed much since the 1970s. Think about it – the 1970s! It’s no secret that extract, transfer, and load (ETL) processes remain the bane of data consumers’ existence, largely because many enterprises are still using 25-year-old solutions to manage ETL and integrate data.

Cloud Computing

The good news is, data integration is becoming much easier to do, and is well on its way to becoming invisible. Enterprise integration cloud technology promises to replace slow and cumbersome scripting and manual data movement with fast, open, seamless data pipelines, optimized with AI techniques.

Remember how, as Internet use exploded in the late 1990s, the tech industry was abuzz with companies offering all manner of optimization technologies, like load balancing, data mirroring, and throughput optimization? These days you never hear about these companies anymore; we take high-performance internet service for granted, like the old-fashioned dial tone.

I am confident that we are embarking on a similar era for enterprise data integration, one in which modern, cloud-first technologies will make complex data integration processes increasingly invisible, seamlessly baked into the way data is stored and accessed.

Making history with data integration

I had the pleasure of meeting Walt some years ago at his office, a miniature museum with many of the personal tech industry’s greatest inventions on display. There, his love of tech was apparent and abundant. Apple IIe? Nokia Communicator 9000? Palm Treo and original iPod? Of course. If Walt were to be at his keyboard, in his office, for another couple of years, I’m pretty sure his collection would be joined by a technology with no physical form factor, but of even greater import: the enterprise cloud.

Hats off to you, Walt. And while you may have given your final sign-off, “Mossberg out,” enterprise tech is most definitely still in.

Follow me on Twitter @gdhillon.

Gaurav Dhillon is CEO of SnapLogic. You can follow him on Twitter @gdhillon.

SnapLogic recognized by Mogul as top workplace for millennial women

By Laura Selig

At SnapLogic, we know that a diverse workplace powers innovation and drives growth. Only by bringing in people with different backgrounds, experiences, and perspectives can we truly build an innovative company for the ages.

So we were thrilled to learn that SnapLogic has been recognized as a top employer for millennial women by Mogul, a leading technology platform and recruiting site that enables professional women around the world to connect, share information and knowledge, and network. We’re proud to report that SnapLogic was ranked #21 on Mogul’s list of the Top 100 Companies For Millennial Women in 2017.

Based on two years of interviews, surveys, and research, Mogul set out to identify the top 100 companies worldwide that are actively leading initiatives to achieve gender equity in the workplace. Mogul notes, with millennials increasingly joining and impacting the workforce, these 100 companies “collectively have the opportunity to shape workplace innovation and accelerate cultural transformation.”

At SnapLogic, more than 30 percent of our global employees in engineering or technical roles are women – higher than the industry average – and we’re working hard to drive this number even higher by fostering a work environment that is focused on inclusiveness and continuous career development. Additionally, SnapLogic’s affinity group, Women@SnapLogic, provides an internal networking group for women to build community and discuss topics relevant to women in the workplace. Female engineers at SnapLogic are working on breakthrough projects in cloud computing, big data, artificial intelligence, Internet of Things, and more, helping to drive our innovation agenda forward.

Are you passionate about technology and looking to put your skills and experience to work at a dynamic, innovative enterprise software upstart? Be sure to check out SnapLogic’s current job openings.

Laura Selig is Vice President, People at SnapLogic.

 

Will the Cloud Save Big Data?

This article was originally published on ITProPortal.

Employees up and down the value chain are eager to dive into big data solutions, hunting for golden nuggets of intelligence to help them make smarter decisions, grow customer relationships and improve business efficiency. To do this, they’ve been faced with a dizzying array of technologies – from open source projects to commercial software products – as they try to wrestle big data to the ground.

Today, a lot of the headlines and momentum focus around some combination of Hadoop, Spark and Redshift – all of which can be springboards for big data work. It’s important to step back, though, and look at where we are in big data’s evolution.

In many ways, big data is in the midst of transition. Hadoop is hitting its pre-teen years, having launched in April 2006 as an official Apache project – and then taking the software world by storm as a framework for distributed storage and processing of data, based on commodity hardware. Apache Spark is now hitting its strides as a “lightning fast” streaming engine for large-scale data processing. And various cloud data warehousing and analytics platforms are emerging, from big names (Amazon Redshift, Microsoft Azure HDInsight and Google BigQuery) to upstart players like Snowflake, Qubole and Confluent.

The challenge is that most big data progress over the past decade has been limited to big companies with big engineering and data science teams. The systems are often complex, immature, hard to manage and change frequently – which might be fine if you’re in Silicon Valley, but doesn’t play well in the rest of the world. What if you’re a consumer goods company like Clorox, or a midsize bank in the Midwest, or a large telco in Australia? Can this be done without deploying 100 Java engineers who know the technology inside and out?

At the end of the day, most companies just want better data and faster answers – they don’t want the technology headaches that come along with it. Fortunately, the “mega trend” of big data is now colliding with another mega trend: cloud computing. While Hadoop and other big data platforms have been maturing slowly, the cloud ecosystem has been maturing more quickly – and the cloud can now help fix a lot of what has hindered big data’s progress.

The problems customers have encountered with on-premises Hadoop are often the same problems that were faced with on-premises legacy systems: there simply aren’t enough of the right people to get everything done. Companies want cutting-edge capabilities, but they don’t want to deal with bugs and broken integrations and rapidly changing versions. Plus, consumption models are changing – we want to consume data, storage and compute on demand. We don’t want to overbuy. We want access to infrastructure when and how we want it, with just as much as we need but more.

Big Data’s Tipping Point is in the Cloud

In short, the tipping point for big data is about to happen – and it will happen via the cloud. The first wave of “big data via the cloud” was simple: companies like Cloudera put their software on Amazon. But what’s “truly cloud” is not having to manage Hadoop or Spark – moving the complexity back into a hosted infrastructure, so someone else manages it for you. To that end, Amazon, Microsoft and Google now deliver “managed Hadoop” and “managed Spark” – you just worry about the data you have, the questions you have and the answers you want. No need to spin up a cluster, research new products or worry about version management. Just load your data and start processing.

There are three significant and not always obvious benefits to managing big data via the cloud: 1) Predictability – the infrastructure and management burden shifts to cloud providers, and you simply consume services that you can scale up or down as needed; 2) Economics – unlike on-premises Hadoop, where compute and storage were intermingled, the cloud separates compute and storage so you can provision accordingly and benefit from commodity economics; and 3) Innovation – new software, infrastructure and best practices will be deployed continuously by cloud providers, so you can take full advantage without all the upfront time and cost.

Of course, there’s still plenty of hard work to do, but it’s more focused on the data and the business, and not the infrastructure. The great news for mainstream customers (well beyond Silicon Valley) is that another mega-trend is kicking in to revolutionize data integration and data consumption – and that’s the move to self-service. Thanks to new tools and platforms, “self-service integration” is making it fast and easy to create automated data pipelines with no coding, and “self-service analytics” is making it easy for analysts and business users to manipulate data without IT intervention.

All told, these trends are driving a democratization of data that’s very exciting – and will drive significant impact across horizontal functions and vertical industries. Data is thus becoming a more fluid, dynamic and accessible resource for all organizations. IT no longer holds the keys to the kingdom – and developers no longer control the workflow. Just in the nick of time, too, as the volume and velocity of data from digital and social media, mobile tools and edge devices threaten to overwhelm us all. Once the full promise of the Internet of Things, Artificial Intelligence and Machine Learning begins to take hold, the data overflow will be truly inundating.

The only remaining question: What do you want to do with your data?

Ravi Dharnikota is the Chief Enterprise Architect at SnapLogic. 

Cloud fluency: Does your data integration solution speak the truth?

There’s been a lot of cloud-washing in the enterprise data integration space — vendors are heavily promoting their hybrid cloud services, yet for many, only a skinny part of their monolithic apps has been “cloudified.”

In an era of “alternative facts,” it’s important to make technology decisions based on truths. Here is an important one: A big data integration solution built on genuine, made-for-the-cloud platform as a service (PaaS) technology offers important benefits including:

  1. Self-service integration by “citizen integrators,” without reliance on IT
  2. For IT organizations, the ability to easily connect multiple data sets, to achieve a bespoke enterprise tech environment

These are in addition to the traditional benefits of cloud solutions: no on-premise installation; continuous, no-fuss upgrades; and the latest software innovation, delivered automatically.

Why “built for the cloud” matters

You can’t get these benefits with “cloudified” software that was originally invented in 1992. Of course, I’m referring to Informatica; while the company promotes its cloud capabilities, the software largely retains a monolithic architecture that resides on-premises, and does most of its work there, too.

In contrast, SnapLogic is purpose-built for the cloud, meaning there are no legacy components that prevent the data and application integration service from running at cloud speed. Data streams between applications, databases, files, social and big data sources via the Snaplex, a self-upgrading, elastic execution grid.

In more everyday terms, SnapLogic has 100% cloud fluency. Our technology was made for the cloud, born in the cloud, and it lives in the cloud.

The consumerization of data integration

Further to point 1 above, “citizen integrators,” industry futurists like R. “Ray” Wang have been talking about the consumerization of IT for more than half a decade. And that is exactly what SnapLogic has mastered. Our great breakthrough, our big innovation, is that we have consumerized the dungeon-like, dark problem of data integration.

Integration used to be a big, boring problem relegated to the back office. We’ve brought it from the dungeon to the front office and into the light. It is amazing to see how people use our product. They go from one user to hundreds of users as they get access to data in a secure, organized and appropriately access-controlled manner. But you don’t have a cast of thousands of IT people enabling all this; users merely help themselves. This is the right model for the modern enterprise.

“An ERP of one”

As for the second major benefit of a true cloud solution — a bespoke enterprise tech environment, at a fraction of the time and cost of traditional means — here’s a customer quote from a young CEO of a hot company that’s a household name.

“Look, we’ve got an ‘ERP of one’ by using SnapLogic — a totally customized enterprise information environment. We can buy the best-of-the-best SaaS offerings, and then with SnapLogic, integrate them into a bespoke ERP system that would cost a bajillion dollars to build ourselves. We can custom mix and match the capabilities that uniquely fit us. We got the bespoke suit at off-the-rack prices by using SnapLogic to customize our enterprise environment.”

To my mind, that’s the big payoff, and an excellent way to think about SnapLogic’s value. We are able to give our customer an “ERP of one” faster and cheaper than they could have ever imagined. This is where the world is going, because of the vanishingly low prices of compute power and storage, and cloud computing.

Today you literally can, without a huge outlay, build your own enterprise technology world. But you need the glue to realize the vision, to bring it all together. That glue is SnapLogic.

Find out more about how and why SnapLogic puts best-of-breed enterprise integration within every organization’s grasp. Register for this upcoming webinar featuring a conversation with myself, industry analyst and data integration expert David Linthicum, and Gaurav Dhillon, SnapLogic’s CEO and also an Informatica alumnus: “We left Informatica. Now you can, too.”

SNP_Thumb_Informatica

Gaurav Dhillon is CEO at SnapLogic. Follow him on Twitter @gdhillon.

Enterprise IoT: Watching Cat Videos Without Getting Caught (or, How I Learned to Stop Looking Over My Shoulder and Trust the CEO Proximity Alert

We have a slight problem at SnapLogic. While we spend a vanishingly small percent of the day watching adorable cat videos on the Internet, it seems our CEO always shows up behind our desks while doing so. If only we knew when our CEO was nearby and could get an alert when he was.

Continue reading “Enterprise IoT: Watching Cat Videos Without Getting Caught (or, How I Learned to Stop Looking Over My Shoulder and Trust the CEO Proximity Alert”