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	<title>interview Archives - CDInsights</title>
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	<title>interview Archives - CDInsights</title>
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		<title>Is Tech Changing the World or Is the World Changing Tech?</title>
		<link>https://www.clouddatainsights.com/is-tech-changing-the-world-or-is-the-world-changing-tech/</link>
					<comments>https://www.clouddatainsights.com/is-tech-changing-the-world-or-is-the-world-changing-tech/#respond</comments>
		
		<dc:creator><![CDATA[Elisabeth Strenger]]></dc:creator>
		<pubDate>Thu, 08 Jun 2023 20:53:28 +0000</pubDate>
				<category><![CDATA[AI/ML]]></category>
		<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[ChatGPT]]></category>
		<category><![CDATA[digital transformation]]></category>
		<category><![CDATA[interview]]></category>
		<guid isPermaLink="false">https://www.clouddatainsights.com/?p=3295</guid>

					<description><![CDATA[Change might be setting a new speed record and it all comes down to data–more insights from data, faster insights, performance that allows the average human to interact with the corpora of the world’s information in a real-time context. At Gartner’s Data &#038; Analytics Summit Qlik’s vice president of product marketing, Dan Potter, traces changes in how data is integrated, stored, and delivered.]]></description>
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<figure class="aligncenter size-full is-resized"><img fetchpriority="high" decoding="async" src="https://www.clouddatainsights.com/wp-content/uploads/2023/06/Depositphotos_12260414_S.jpg" alt="" class="wp-image-3296" width="750" height="450" srcset="https://www.clouddatainsights.com/wp-content/uploads/2023/06/Depositphotos_12260414_S.jpg 1000w, https://www.clouddatainsights.com/wp-content/uploads/2023/06/Depositphotos_12260414_S-300x180.jpg 300w, https://www.clouddatainsights.com/wp-content/uploads/2023/06/Depositphotos_12260414_S-768x461.jpg 768w" sizes="(max-width: 750px) 100vw, 750px" /></figure></div>


<p>Given the recent pandemic, the state of the workforce, and unpredictable economic conditions, the world has changed. Technology has been part of that change by making rapid digitization of business possible, serving up masses of data that could be sifted by ML and analytics for signals of future behaviors. Into this volatile mix drops ChatGPT, accelerating change even while organizations are still trying to determine its value and its risks.&nbsp;</p>



<p>Qlik’s Vice President of Product Marketing, Dan Potter, has a front-row seat as he watches how customers and partners are making buying and design decisions for data and analytics solutions. (See his bio below.) He shared his observations with Cloud Data Insights (CDI) at the Gartner Data &amp; Analytics Summit in March. One thought he shared is that history is still a powerful indicator of what might happen in the future.</p>



<p><strong>Dan:</strong> Shall we have this interview done with ChatGPT? You ask ChatGPT a question and then just attribute the answer to me. That way, I might sound smarter than I am. I tried writing a session abstract using ChatGPT and I’d say it was about 95% perfect. ChatGPT is going to change everything.</p>



<p><strong>CDI: Speaking of change, how have customers&#8217; needs or attitudes changed given the pandemic-fueled acceleration of digital transformation? Are current economic conditions influencing customer conversations?&nbsp;</strong></p>



<p><strong>Dan: </strong>Absolutely but let’s first step back for a minute and look at the major trend we&#8217;re in–-movement to the cloud. We&#8217;ve hit that point where it&#8217;s becoming much more mature, and people are comfortable moving data into the cloud and processing data in the cloud, from an integration perspective. Now you&#8217;re starting to see people behave a little differently. They’re no longer as interested in picking come open-source pieces and assembling an ingestion piece, transformation pieces, etc. People are looking for more solutions to solve a different level of problem rather than how to build an integration stack. Buying behavior has changed and people are taking a more holistic view and asking themselves, “How do I solve this business problem in a more modern way?”&nbsp;</p>



<p>The other part of the change that we&#8217;ve seen particularly in the last year was a focus on FinOps and how to best optimize spend because the move to consumption-based pricing makes prediction harder, especially of computing costs. In the end, a FinOps app is an analytics application.</p>



<p>The nice thing with cloud is you can start off small, but you scale up really quickly. So people are faced with bills that they didn&#8217;t realize they were going to incur in some of these projects. They&#8217;re starting to want to have visibility into where they are spending their money. What part of the process is costing more and how do we get smart about doing some of this stuff? If I&#8217;m building a cloud data warehouse and I&#8217;m moving data from a lot of different sources, I incur cost. When I start to apply and merge that data into the warehouse, that’s a compute cost. So if you&#8217;ve got a table that gets used only on a weekly basis, don&#8217;t spend money to have it continuously updated. When you know, it&#8217;s only going to be updated. It&#8217;s only gonna be used once a week. So being smart about turning the knobs. Another one is the transformation of that data. If this is something that needs to be done frequently, do it frequently, but if it&#8217;s not, don&#8217;t pay for it.&nbsp;</p>



<p>In addition to visibility into usage and cost, you need different approaches for landing data. Qlik does continuous real-time change-data capture. Data is moved in real time, it lands into the data warehouse in real time,&nbsp; but you don’t have to apply it in real time. You may have teams that need real real-time operational views of that data so you create a view to that data, we don&#8217;t materialize the view. And so people have the benefit of real time, but it hasn&#8217;t been applied yet to the warehouse; they haven’t incurred the cost of applying data.There are some techniques like this that people are starting to get wiser about.&nbsp;</p>



<p><strong>CDI: Would you say that instead of collecting new data to feed into FinOps analysis, you can leverage your platform’s operating data for insights into usage patterns and make some predictions based on past utilization?</strong></p>



<p><strong>Dan:</strong> <em>And </em>what’s <em>not </em>being used by looking at query logs from the different systems. Many enterprise customers, are doing chargebacks for IT services anyway. The data engineering team is keeping close tabs on what they create and how it gets utilized. They&#8217;re capturing this kind of data already for some systems. They can broaden their practice.</p>



<p>The next step is to apply machine learning and get really intelligent about predictions and recommendations, for example, to automate the process of when and where to transform data. It might make sense to do transformation and generate the SQL in the warehouse itself as opposed to doing it on the client before you even move the data. Two factors will determine which approach to use–meeting the business requirement and reducing cost.</p>



<p><strong>CDI: This level of embedded advisory service is still a work in progress, right? Are there other parts of your product roadmap that speak to dual requirements of meeting business needs and containing cloud costs?</strong></p>



<p><strong>Dan:</strong> It’s not all new technology. For example, the Qlik Cloud for Data Integration is based on the best pieces of our client-managed technologies offered in the cloud as a fully-managed solution. Automation is one of the capabilities we’ve included in that. We’ve automated transformation and the creation of data marts is a push-button operation–no data prepping like putting it into an analytics format, no SQL or scripting. We generate the SQL, we push it into the cloud warehouse, and it just runs continuously. That&#8217;s game-changing. We heard at the Summit keynote that the number one challenge is skilled people. How do you overcome that? Automation.&nbsp;</p>



<p><strong>CDI: It does seem that the no-code, low-code, automation conversation has heated up recently. It’s no longer a topic for just software developers. Data teams see it as a productivity measure and CDOs see it as a self-service / democratization approach.&nbsp;</strong></p>



<p><strong>Dan: </strong>Is it no code, low code, automation, or <em>augmentation</em>? Assisting the human in the tasks that they do is definitely one of the goals. And that&#8217;s where ChatGPT is going to play a role.</p>



<p><strong>CDI: What role do you see ChatGPT playing in your product roadmap?</strong></p>



<p><strong>Dan: </strong>We think that generative AI is really, really interesting. Automating the generation of code like SQL is really interesting to us. We haven&#8217;t announced anything in the roadmap yet, but we demonstrated some of it today in the BI Bake Off from an analytics perspective.&nbsp;</p>



<p>On the data integration side, we need to find the right way in which to use generative AI because we&#8217;re selling to people who code for a living, so we want to augment their work, not replace it. And automate the mundane tasks so they can focus on the higher-value work. Instead of generative AI, we look at the promise of co-generation through ChatGPT. My son and I used it to build a website with a map of the United States where each state was clickable and could show news stories from that state. It just took a couple of runs to get the working HTML</p>



<p>I think generative AI will progress with organizations like ours creating our own language models and training them on our own unique content or our user knowledge base–our collective learning. If you build your own ChatGPT-like service you can provide a lot of value that a generic ChatGPT that’s an amalgamation of everything put into a large language model cannot provide.</p>



<p><strong>CDI: What challenges are your customers facing now?</strong></p>



<p><strong>Dan: </strong>The same kind of challenges they&#8217;ve been faced with for the past 30 years. Everything new that comes into play does not do away with the old themes or necessarily solve the existing problems. I think the biggest challenge though is the many kinds of source data that need to be integrated. Sensor data, unstructured data, etc. are coming into play. And the volume of data is becoming much larger. The techniques for working with this complexity keep evolving but they don’t keep pace and it’s still a struggle to make data analytics-ready and business-ready.</p>



<p>I look at the data architectures and data management changes in the last 20 years. We went from enterprise data warehouses, which were SQL, and very rigid and very structured but were supposed to solve everything. Then they became too rigid and we moved to Hadoop, which was going to solve everything and data warehouses would go away. Next came data lakes into which the enterprise data warehouse was dumped it, along with the Hadoop data and new data sets. The data lake was going to solve everything but no, people were having a hard time getting value from it. Then all sudden Cloud Data Warehouses came in and SQL was back in vogue.&nbsp;</p>



<p>The technology has changed but&nbsp; the challenges are still the same kinds of challenges:&nbsp; unlock the data, make it ready for me to use, how do I get the insights that I need? How do I take action on those insights?&nbsp;</p>



<p><strong>CDI: What emerging technology or business trend do you think will have the most impact on your company or your customers? Is it ChatGPT?</strong></p>



<p><strong>Dan: </strong>I think generative AI is going to it certainly had certainly have the biggest mindshare. We are&nbsp; just starting to scratch the surface. What business value it starts to deliver, I think we don&#8217;t even know. But I definitely think that&#8217;s going to have a huge impact on everything we do. Not only in integration and analytics, but in our everyday work as we start to integrate this functionality into the daily productivity apps.</p>



<p>Another thing we can say is that we&#8217;re back into the world of no predictions.</p>



<p><em><strong>Bio:</strong> Dan Potter is the vice president of product marketing at Qlik. He is responsible for Qlik’s go-to-market strategies for modern data architectures, data integration and DataOps. Dan brings more than 25 years of leadership and marketing management having previously held executive positions at Oracle, IBM, Attunity and Progress Software. He helped accelerate industry-leading revenue growth in data integration at Attunity and post-acquisition at Qlik.  He is a published author and frequent speaker on cloud modernization, data management and analytics.</em></p>
<div class="saboxplugin-wrap" itemtype="http://schema.org/Person" itemscope itemprop="author"><div class="saboxplugin-tab"><div class="saboxplugin-gravatar"><img alt='Elisabeth Strenger' src='https://secure.gravatar.com/avatar/d42bdc4339b8a684f54ad42d3ac0accb?s=100&#038;d=mm&#038;r=g' srcset='https://secure.gravatar.com/avatar/d42bdc4339b8a684f54ad42d3ac0accb?s=200&#038;d=mm&#038;r=g 2x' class='avatar avatar-100 photo' height='100' width='100' itemprop="image"/></div><div class="saboxplugin-authorname"><a href="https://www.clouddatainsights.com/author/estrenger/" class="vcard author" rel="author"><span class="fn">Elisabeth Strenger</span></a></div><div class="saboxplugin-desc"><div itemprop="description"><p>Elisabeth Strenger is a Senior Technology Writer at <a href="https://www.clouddatainsights.com/">CDInsights.ai</a>.</p>
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		<post-id xmlns="com-wordpress:feed-additions:1">3295</post-id>	</item>
		<item>
		<title>Achieving Hyperscale Analysis: A Deep Dive with Ocient</title>
		<link>https://www.clouddatainsights.com/achieving-hyperscale-analysis-a-deep-dive-with-ocient/</link>
					<comments>https://www.clouddatainsights.com/achieving-hyperscale-analysis-a-deep-dive-with-ocient/#respond</comments>
		
		<dc:creator><![CDATA[Elisabeth Strenger]]></dc:creator>
		<pubDate>Fri, 26 May 2023 12:08:19 +0000</pubDate>
				<category><![CDATA[Cloud Data Platforms]]></category>
		<category><![CDATA[cloud costs]]></category>
		<category><![CDATA[data architecture]]></category>
		<category><![CDATA[hyperscale]]></category>
		<category><![CDATA[interview]]></category>
		<guid isPermaLink="false">https://www.clouddatainsights.com/?p=3239</guid>

					<description><![CDATA[CDI sits down with Ocient to talk about the unique needs customers have when it comes to hyperscale analytics.]]></description>
										<content:encoded><![CDATA[<div class="wp-block-image">
<figure class="aligncenter size-full is-resized"><img decoding="async" src="https://www.clouddatainsights.com/wp-content/uploads/2023/05/Depositphotos_619030258_S.jpg" alt="" class="wp-image-3241" width="750" height="415" srcset="https://www.clouddatainsights.com/wp-content/uploads/2023/05/Depositphotos_619030258_S.jpg 1000w, https://www.clouddatainsights.com/wp-content/uploads/2023/05/Depositphotos_619030258_S-300x166.jpg 300w, https://www.clouddatainsights.com/wp-content/uploads/2023/05/Depositphotos_619030258_S-768x425.jpg 768w" sizes="(max-width: 750px) 100vw, 750px" /><figcaption class="wp-element-caption"><em>Hyperscale analytics comes with a unique set of challenges. Ocient outlines what that means.</em></figcaption></figure></div>


<p>What does it take to manage increasingly complex data processing requirements? The kind that artificial intelligence, machine learning, and 5G need? CDInsights&#8217; Elisabeth Strenger sat down with Chris Gladwin, co-founder and CEO of Ocient, and Jenna Boller, Director of Marketing, to talk about where hyperscale analysis stands now and how companies can balance the ever-present question of cost with what it takes to process truly massive amounts of data.</p>



<p><strong>CDI: One of the things that RTInsights and CDInsights are very interested in is the changes in the requirements for performing AI-based actions in an application. What is your experience with what customers require in terms of access rates and results speeds?</strong></p>



<p><strong>Chris Gladwin:</strong> This is something Ocient is really good at. When I started my career, the very first thing I did out of college was work for a big aerospace company. I spent a lot of my career as a &#8220;professional customer,&#8221; evaluating IT products for enterprises and understanding what enterprises need.</p>



<p>It&#8217;s hard for customers to express their requirements, and it&#8217;s hard to hear and interpret them. But I think that&#8217;s what we&#8217;re good at. Most companies are trying to commoditize everything and put it in a cloud form where you can swipe a credit card, and two minutes later, you have a storage container, and off you go. That&#8217;s fine, and a lot of the market wants that.&nbsp;</p>



<p>But there are also times when requirements are complex, and it takes a high bandwidth dialogue. Our average customers are multimillion-dollar customers, and we spend hundreds of hours with them to understand what they want. So our focus is really on hyperscale analysis. The requirements [our customers] were asking us to meet were unmet -– analyzing data at hyperscale — not just storing a petabyte, but analyzing a petabyte every time they run a query on average.&nbsp;</p>



<p>So we focus on &#8220;interactive time,&#8221; which means the time that an analyst or person or an application is waiting for the answer.&nbsp;</p>



<p><strong>See also:</strong> <a href="https://www.clouddatainsights.com/nailing-ai-from-cloud-to-the-edge/">Nailing AI From Cloud to the Edge</a></p>



<p><strong>CDI: Having been in the real-time operating system space in my early days at Red Hat with IoT, I know the formal definitions of real-time are very different from what people are seeing now. So I love the idea of &#8220;interactive time.&#8221; I used to use something called operational time, which is even slower, right?&nbsp;</strong></p>



<p><strong>Gladwin</strong>: They&#8217;re talking like the speed of light for real time. And that&#8217;s easy to do when you&#8217;re pulling a value out of an index, or it&#8217;s in your results cache. What&#8217;s hard is when that isn&#8217;t the case.&nbsp;&nbsp;&nbsp;</p>



<p>When you look under the hood at the database, it&#8217;s not &#8220;go to this location, grab the value, get it back.&#8221; It&#8217;s: &#8220;Oh, I gotta have a million parallel tasks that get kickoff, and then there&#8217;s going to be all these little intermediate result sets to get added together.&#8221;&nbsp;</p>



<p>And you have to do all that, and 2.7 seconds later, here you go. That&#8217;s what we do. That&#8217;s novel. Just five years ago, the hardware that would enable that software to do that did not exist. Yeah, you could solve these problems with billion-dollar supercomputers. That&#8217;s easy. But most people don&#8217;t have a billion dollars or $500 million to spend on a supercomputer.&nbsp;</p>



<p>So then what do you do? The level of parallelization you&#8217;re going to have at every layer–your stack starting down at the memory allocator and going all the way up to the top of the sequel prompt on even a single small cluster. You might have a million parallel tasks in flight. That is a very, very different architecture than anything else that was built before.&nbsp;</p>



<p><strong>Jenna Boller</strong>: The other thing that was mentioned was cost. Where we excel is in continuous analysis and movement of data. So not just the queries, but also loading data, extracting it, and sending it somewhere. That&#8217;s where the cost can get out of control in the cloud. It&#8217;s not just running a report once a day or once a week; it&#8217;s constantly using processing power. So [we] build in some layer of predictability because a lot of our customers are not incentivized to run the analytics they need at scale because of the cost.&nbsp;</p>



<p>We&#8217;re focused on removing that constraint.</p>



<p><strong>CDI:I did a bit of reading on the Ocient cloud. Can you even do the kind of hyperscale analytics you&#8217;re talking about on the three big clouds in the US? Is it even feasible?</strong></p>



<p><strong>Chris</strong>: Sometimes it is, but it would be prohibitively expensive.</p>



<p>See also: <a href="https://www.clouddatainsights.com/how-enterprises-are-benefitting-from-mls-promise/">How Enterprises are Benefitting from ML&#8217;s Promise</a></p>



<p><strong>CDI: Consistency and predictability are vitally important. So is there anything you&#8217;d like to talk about in terms of why? Something like the Ocient cloud, or if you&#8217;re a US government, your whole cloud? Why is that a value to customers working at hyperscale?</strong></p>



<p><strong>Chris</strong>: The predictability of knowing what you&#8217;re going to spend is super important. It&#8217;s not good for anyone&#8217;s career to implement something and then come rolling in with the build at five times what was budgeted. If it&#8217;s usage-based, that can and does happen when you start doing hyperscale analysis. The other is that the cost will be much lower for an Ocient system. One reason is we physically have to do less stuff.&nbsp;</p>



<p>We think of everything in terms of what we call an entitlement. We look at what all the hardware is physically capable of. You can always solve the problem with money, but how do you optimize costs? Our job as software engineers is to keep everything at 85% capacity, but that costs money. So one thing is you are wildly efficient in using every available resource.&nbsp;</p>



<p>The other thing is there are some techniques that we employ that also save cost — one of them is zero-copy reliability. This was a set of techniques that we developed that Cleversafe, my prior company, has patents on. Instead of making copies, we virtualized data. We encode and decode in real time as you write and read. Copies are expensive, especially when it&#8217;s a petabyte or an exabyte. If you want to maintain a given level of reliability, you have to make an accelerating number of copies. It is much cheaper to use math to encode and decode in real time.&nbsp;&nbsp;</p>



<p>We encode and decode data as needed to get reliability like we&#8217;re making multiple copies. But generally speaking, the total amount of physical storage we need is not three times or five times. It&#8217;s 1.3 times to start with, and that saves huge money. Our design goal is to have the most price-performance possible. As a result, we were able to do that.</p>



<p><strong>CDI: Early adopters of non-scientific computing hyperscale were the telcos and ad tech seems, but who do you see as the next industries to be interested in hyperscale processing analytics?</strong></p>



<p><strong>Chris</strong>: One area we&#8217;re seeing is automotive. Every time a new car is produced in a country like the United States, it&#8217;s replacing, on average, a car that&#8217;s 20 years old and doesn&#8217;t make a lot of data. But the new car has hundreds of computers in there, and it makes tons and tons of data.&nbsp;</p>



<p>So that industry is shifting and will become even more so one of the largest creators of data. The fleet vehicle manufacturers themselves— as they&#8217;re transitioning from internal combustion to electrical technologies—know everything there is to know about internal combustion engines, but they&#8217;re just learning about batteries and electric power. It&#8217;s easy to build a car that runs on a battery. What&#8217;s hard is how to manufacture hundreds of millions of these super high quality [cars], with different lithium that has been mined from different places at different times, different weather conditions, and different driving patterns, and make all that reliable all the time? The only way you can look at all that data is hyperscale.</p>



<p><strong>Jenna</strong>: And there&#8217;s a knock-on effect. Once one thing is transformed, things around it get transformed, and then they&#8217;re leveraging more data as well.</p>



<p><strong>Chris</strong>: When I speak generally on data growth, I always challenge the audience to name one thing where the new version makes less data than the old version. So far, no one&#8217;s met the challenge.</p>



<p>It&#8217;s this never-ending acceleration. And you can&#8217;t stop it. In every enterprise, there&#8217;s the line of business people, and they&#8217;re like, &#8220;Oh my God, this new microscope is amazing. Let&#8217;s use it.&#8221; And then the IT people always get stuck with the same thing: oh, it&#8217;s ten times as much. You don&#8217;t get ten times as much budget, but you&#8217;ve got to figure it out. That is always the dilemma of IT.</p>



<p><strong>CDI: And metadata is increasing too. That&#8217;s completely hidden from the business user. What do you see as the next big trend or challenge that will affect your customers or even your company&#8217;s direction?</strong></p>



<p><strong>Chris</strong>: 5g — What consumers will experience is a big speed increase, and it opens up all these new kinds of high res video capabilities like that. But what the telcos face is the first major upgrade of the whole back-end infrastructure for decades. It&#8217;s gonna go faster. They&#8217;ll know much more about what&#8217;s happening in the network and have much more ability to route traffic, manage it, and optimize it.&nbsp;</p>



<p>But that comes at the cost of the amount of data they have to deal with. It just went up by at least an order of magnitude. So from a telecom network performance optimizer, this is amazing. But then there&#8217;s somebody else at the telecom whose job is to be the data analysis platform. Team, and they&#8217;ve got a huge challenge ahead of them. The scale just exploded. So that&#8217;s the big thing we see. And I&#8217;d argue it&#8217;s the largest capital investment in human history. It&#8217;s in the trillions of dollars.</p>



<p>I don&#8217;t know what number two is. So it&#8217;s a big deal, and it affects everything. It affects telcos. It affects airlines. Everything will be affected by 5g.</p>



<p><strong>See also:</strong> <a href="https://www.clouddatainsights.com/the-data-lakehouse-takes-center-stage-insights-from-dremios-co-founder/">The Data Lakehouse Takes Center Stage: Insights from Dremio&#8217;s Co-Founder</a></p>



<p><strong>Chris</strong>: I&#8217;m going to make one concluding point. What&#8217;s exciting for Ocient right now is we spent years creating this new architecture and coming out with an initial product a couple of years ago. But the phase we&#8217;re in now is where each release, each customer engagement, offers major new chunks of functionality. So it&#8217;s a really fun time for us, and it&#8217;s fun for our customers too because [we&#8217;re doing] cool new stuff that was never possible.</p>



<p><strong>Jenna</strong>: I also just wanted to clarify one thing. We do enable our customers to run in the cloud. When we&#8217;re architecting at hyperscale, we have to look for every efficiency. At the petabyte scale, inefficiency obviously can create so much more waste. So whether it&#8217;s consolidating more workloads and data into Ocient and not having to copy the data as much — that&#8217;s where you can see we&#8217;ve done a lot to drive efficiencies. So if you are running Ocient in Google Cloud or AWS, you&#8217;re getting the benefit of lowering your cost that way because it&#8217;s a more efficient platform engine.&nbsp;</p>



<p><strong>CDI: They don&#8217;t only get the benefit in the Ocient cloud; they also receive those benefits in other clouds.</strong></p>



<p><strong>Jenna</strong>: We&#8217;re deployment agnostic. Our data centers run on 100% renewable energies. We just wanted to open up whatever works best for our customers.</p>
<div class="saboxplugin-wrap" itemtype="http://schema.org/Person" itemscope itemprop="author"><div class="saboxplugin-tab"><div class="saboxplugin-gravatar"><img alt='Elisabeth Strenger' src='https://secure.gravatar.com/avatar/d42bdc4339b8a684f54ad42d3ac0accb?s=100&#038;d=mm&#038;r=g' srcset='https://secure.gravatar.com/avatar/d42bdc4339b8a684f54ad42d3ac0accb?s=200&#038;d=mm&#038;r=g 2x' class='avatar avatar-100 photo' height='100' width='100' itemprop="image"/></div><div class="saboxplugin-authorname"><a href="https://www.clouddatainsights.com/author/estrenger/" class="vcard author" rel="author"><span class="fn">Elisabeth Strenger</span></a></div><div class="saboxplugin-desc"><div itemprop="description"><p>Elisabeth Strenger is a Senior Technology Writer at <a href="https://www.clouddatainsights.com/">CDInsights.ai</a>.</p>
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		<post-id xmlns="com-wordpress:feed-additions:1">3239</post-id>	</item>
		<item>
		<title>How Enterprises are Benefitting from ML&#8217;s Promise</title>
		<link>https://www.clouddatainsights.com/how-enterprises-are-benefitting-from-mls-promise/</link>
					<comments>https://www.clouddatainsights.com/how-enterprises-are-benefitting-from-mls-promise/#respond</comments>
		
		<dc:creator><![CDATA[Elisabeth Strenger]]></dc:creator>
		<pubDate>Fri, 19 May 2023 12:19:14 +0000</pubDate>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[data democratization]]></category>
		<category><![CDATA[interview]]></category>
		<guid isPermaLink="false">https://www.clouddatainsights.com/?p=3148</guid>

					<description><![CDATA[“Data Democratization” was an explicit goal of many attending this year’s Gartner Data &#038; Analytics Summit–last year it was an aspiration. Two of Altair’s SVPs reflect on the technology investments and cultural shift that make it possible.]]></description>
										<content:encoded><![CDATA[<div class="wp-block-image">
<figure class="aligncenter size-full is-resized"><img loading="lazy" decoding="async" src="https://www.clouddatainsights.com/wp-content/uploads/2023/05/Depositphotos_205044244_S.jpg" alt="" class="wp-image-3149" width="750" height="500" srcset="https://www.clouddatainsights.com/wp-content/uploads/2023/05/Depositphotos_205044244_S.jpg 1000w, https://www.clouddatainsights.com/wp-content/uploads/2023/05/Depositphotos_205044244_S-300x200.jpg 300w, https://www.clouddatainsights.com/wp-content/uploads/2023/05/Depositphotos_205044244_S-768x512.jpg 768w, https://www.clouddatainsights.com/wp-content/uploads/2023/05/Depositphotos_205044244_S-930x620.jpg 930w" sizes="(max-width: 750px) 100vw, 750px" /><figcaption class="wp-element-caption"><em>What does it take to achieve data democratization? Altair has a few ideas.</em></figcaption></figure></div>


<p>Data democratization is a multi-faceted initiative frequently led by creating a systems architecture that allows for the actual or virtual pooling of data. Then there are countless adjacent areas to adjust such as security, privacy, governance, and performance to name a few. It requires a sizeable investment and commitment to change the organizational culture around data usage and build a sustainable infrastructure–at enormous scale. There are many conversations around the computing and data storage scale required to handle workloads that might now access petabytes of data instead of terabytes. Now the user-count must scale along with the number of users expecting answers returned in 1 to 3 seconds. Considerations of scale have just grown exponentially.</p>



<p>The Gartner Data &amp; Analytics Summit gathers leading industry analysts, major technology providers, and data strategists from all industry and government sectors. It’s where everyone goes to take the pulse of the market. Altair’s SVP of data analytics, Mark Do Couto, and Ingo Mierswa, SVP of product development shared their read on what customers are seeking to accomplish with advanced analytics and machine learning. Cloud Data Insights (CDI) thought that talking to these executives who bring a record of HPC innovation together with a platform for radically opening data analytics to a broader circle of users would lead to some unique perspectives.</p>



<p><strong>CDI: How have your customers’ needs or attitudes changed given current economic conditions and pandemic-driven digital transformation?</strong></p>



<p><strong>Mark:</strong> As businesses continue to expand their operations and move toward digitalization, software purchases have become increasingly important. However, customers are no longer satisfied with simply purchasing software for the sake of having it. Instead, they are now focused on ROI and are leveraging data insights to make informed business decisions. This has led to a shift in emphasizing the value of software and the benefits that it can provide to businesses.</p>



<p>&nbsp;As businesses generate more and more data, it has become essential to have the right tools and software in place to make sense of it all. They are looking for ways to enable more people in the enterprise to take advantage of data by encouraging the citizen data scientist who does not need to know how to program or code to gain insights from data. By leveraging these insights, businesses can make more informed decisions and take proactive steps to improve their operations. Ultimately, this has led to a more data-driven approach to business decision-making. &nbsp;</p>



<p><strong>CDI: How are you responding? Have you adjusted your product strategy or roadmap? Or your approach to sales?</strong></p>



<p><strong>Mark:</strong> At Altair, we continue to focus on building tools to facilitate this shift. The data analytics team is working on best-in-class data solutions and becoming trusted partners with our customers.</p>



<p>Especially post-pandemic, our customers want as much value from their vendors as possible. &nbsp;So we have a unique licensing model which allows them to have full access to Altair RapidMiner, our data analytics and AI platform, while also taking advantage of our more than 150 individual products.</p>



<p><strong><em>See also:</em></strong>&nbsp; <a href="https://www.rtinsights.com/operationalizing-data-and-analytics/">Operationalizing Data and Analytics</a>&nbsp;</p>



<p><strong>CDI: What are the main technical challenges your customers face? Does this mark a change from a year ago?</strong></p>



<p><strong>Ingo: </strong>Our customers appreciate the massive acceleration and value that can be achieved with data analytics in general, and machine learning (ML) and artificial intelligence (AI) in particular. But advanced analytics can be daunting and often organizations do not know where to start or they have high-value use cases but not the skills in-house to drive the adoption of ML and AI to solve those.<br><br>So while this is related to technical challenges as a result of the complexity of this field, it is as much an organizational or people challenge. How can we empower more people to automate decisions? How can we train employees to ask questions they never dared to ask? We have all the technical solutions for doing so. Just consider the recent breakthroughs of generative AI and large language models. But we need to empower anybody to move fast so that competitors are not getting too much of a competitive edge.</p>



<p>We call this Frictionless AI. Moving to the insights you want faster than ever. To remove all the friction points – technical, cultural, or people-related – customers need to invest in the right platform, their people, and into bringing together their data, their people, and their expertise. Only then can advanced analytics be used at its fullest potential.</p>



<p>The complexity of AI did not change much compared to a year ago. But what did change is the competitive pressure and organizations realize very quickly that they need to move fast to keep up or, even better, lead ahead of their competition.</p>



<p><strong><em>See also:</em></strong>&nbsp; <a href="https://www.rtinsights.com/frictionless-ai-the-key-to-delivering-on-ais-promise/">Frictionless AI: The Key to Delivering on AI’s Promise</a></p>



<p><strong>CDI: Which emerging technology or business trend do you think will have the most impact on your company?</strong></p>



<p><strong>Ingo: </strong>For most team members, generative AI and large language models will have the biggest day-to-day impact. Repetitive tasks will be solved faster than ever and the combination of human and machine knowledge and creativity will deliver larger impact faster than ever. We will keep empowering anybody to make use of these and other technologies in the easiest way possible.<br><br>The other exciting area is what some people call Composite AI. This means that we will see a convergence of machine learning with natural language processing, simulation, and optimization among others. This brings advanced analytics closer to the business problems because machine learning models become the full solution, not just a part of one. The convergence of these fields will help our customers get the most out of a composite solution.</p>



<p><strong>CDI: One of our observations is the sharp increase in the adoption of real-time analytics. First came the emphasis on streaming data where real-time ingestion was an obvious requirement. But how quickly could that real-time data be utilized? Also in real-time? What are you seeing among your customers?</strong></p>



<p><strong>Mark: </strong>That was one of the reasons Altair acquired Data Watch, which was focused on real-time streaming data and real-time data preparation. Those naturally would eventually lead to real-time utlization and then real-time analytics.</p>



<p><strong>Ingo:</strong> At the end of the day, it&#8217;s the customers who have really driven the real-time use case. The faster a customer can get a response, whether it&#8217;s an approval for a credit card, or to make a payment at the register to buy a laptop, the better the customer experience is, the faster a business can transact.</p>



<p>Now, I think it&#8217;s important that we still review that data and make sure the models are being augmented with the real-time data coming, but I think the ability to leverage that real-time data with the models that are being built to help make those business decisions are what customers are looking for.</p>



<p><strong>CDI: How does the need for human review or validation of models affect automation, at least in the case of ML-enriched transactions?</strong></p>



<p><strong>Ingo: </strong>This maybe is connected to the maturity of machine learning overall. I think many people are still wondering where&#8217;s the value generated from machine learning models? They believe that if the models could reveal one super-smart piece of advice that billions of dollars will emerge. That&#8217;s actually not the biggest value of rich machine learning–it&#8217;s really those use cases with tons of “small” decisions, which barely seem to warrant any attention. Yes, you could have hundreds of people observing every single transaction that might look suspicious, but it’s far too expensive to have a human do this. When you automate decisions, it&#8217;s not the one multibillion dollar decision that counts, but it may be billions of small decisions where the value of ML lies. This might also be one consideration to weigh the value of streaming data.</p>



<p><strong><em>See also:</em></strong>&nbsp; <a href="https://www.rtinsights.com/the-hyper-automation-dilemma-reconciling-employee-needs-while-accelerating-growth/">The Hyper-automation Dilemma: Reconciling Employee Needs While Accelerating Growth&nbsp;</a></p>



<p><strong>CDI: Streaming data and “small” decisions bring to mind IoT and edge data. How much have these brought customers to you?</strong></p>



<p><strong>Ingo: </strong>It&#8217;s probably 40% plus and most of the new use cases are actually somewhere connected to IoT sensor data from, for example, from production processes. The usage of this data has gone beyond the predictive maintenance use case. You could also predict the quality of the product you are currently producing. This product isn’t making our quality thresholds, so we probably won’t be able to sell it. Why am I actually still putting energy into this? Maybe I should stop the production process until we improve our yield? You can start putting sensors everywhere and you generate lot of data for this one particular use case. But once the data is available people get more and more creative about other questions they can ask and develop new use cases for the same data. Automotive is one area that we are seeing this phenomenon as lot, and of course with banking and financial services. On-line banking is bringing a lot of existing data into new use cases.</p>



<p><strong>CDI: You’re describing a situation where an organization goes from having one question and a limited set of data points to having so many data points that you can’t grasp the scope of the data but eventually, driven by business people’s and data people’s curiosity, you can weave them together to make much more complex decisions.&nbsp;</strong></p>



<p><strong>Ingo: </strong>That does require a bit of a cultural change and some investment into a platform if you ever want to bring all these data points together for more than a one-off project.</p>



<p>So what do we need to do differently? Maybe empower more people to actually be able to ask their own new questions of data? You’ll find you don’t need to look hard for new use cases, because someone wakes up every single morning and realizes,” wait a second, why are we not&nbsp; using machine learning for this scenario, too. It really transforms how you&#8217;re actually solving problems. Experiencing this happen for an organization is for me, personally on of the most exciting things.</p>



<p><strong>CDI: Are you suggesting that if you have the right platform to bring data together and provide good self-service inquiry or exploration, people will flock to it?</strong></p>



<p><strong>Ingo: </strong>It may sound a little weird for a platform provider to say this, but I don&#8217;t think it&#8217;s the platform. So fantastic platforms or a fantastic piece of technology, which empowers more people is only half of the equation because that&#8217;s not going to change how your employees think about data. The technical solution has to be combined with upskilling your employees for example, making sure that people understand the basic concepts of data science and machine learning. Do they need to know how to build a large language model? No. But would it be useful to know what kinds of use cases can be solved with this zoo of different technologies. And can we bring people to a point where they can translate between a business problem and an analytical approach.&nbsp;</p>



<p>Our customers still surprise us by coming up with use cases which our technology was not intended for.</p>
<div class="saboxplugin-wrap" itemtype="http://schema.org/Person" itemscope itemprop="author"><div class="saboxplugin-tab"><div class="saboxplugin-gravatar"><img alt='Elisabeth Strenger' src='https://secure.gravatar.com/avatar/d42bdc4339b8a684f54ad42d3ac0accb?s=100&#038;d=mm&#038;r=g' srcset='https://secure.gravatar.com/avatar/d42bdc4339b8a684f54ad42d3ac0accb?s=200&#038;d=mm&#038;r=g 2x' class='avatar avatar-100 photo' height='100' width='100' itemprop="image"/></div><div class="saboxplugin-authorname"><a href="https://www.clouddatainsights.com/author/estrenger/" class="vcard author" rel="author"><span class="fn">Elisabeth Strenger</span></a></div><div class="saboxplugin-desc"><div itemprop="description"><p>Elisabeth Strenger is a Senior Technology Writer at <a href="https://www.clouddatainsights.com/">CDInsights.ai</a>.</p>
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		<post-id xmlns="com-wordpress:feed-additions:1">3148</post-id>	</item>
		<item>
		<title>On the Quest for Extremely Fast Data Ingestion</title>
		<link>https://www.clouddatainsights.com/on-the-quest-for-extremely-fast-data-ingestion/</link>
					<comments>https://www.clouddatainsights.com/on-the-quest-for-extremely-fast-data-ingestion/#respond</comments>
		
		<dc:creator><![CDATA[Elisabeth Strenger]]></dc:creator>
		<pubDate>Wed, 01 Mar 2023 23:52:54 +0000</pubDate>
				<category><![CDATA[Data Architecture]]></category>
		<category><![CDATA[data architecture]]></category>
		<category><![CDATA[data ingestion]]></category>
		<category><![CDATA[interview]]></category>
		<category><![CDATA[open-source]]></category>
		<category><![CDATA[storage engine]]></category>
		<guid isPermaLink="false">https://www.clouddatainsights.com/?p=2430</guid>

					<description><![CDATA[CDInsights talks to Speedb about it's decision to move open-source and what goes into the quest for extremely fast data ingestion.]]></description>
										<content:encoded><![CDATA[<div class="wp-block-image">
<figure class="aligncenter size-full is-resized"><img loading="lazy" decoding="async" src="https://www.clouddatainsights.com/wp-content/uploads/2023/02/Depositphotos_132945206_S.jpg" alt="" class="wp-image-2431" width="750" height="500" srcset="https://www.clouddatainsights.com/wp-content/uploads/2023/02/Depositphotos_132945206_S.jpg 1000w, https://www.clouddatainsights.com/wp-content/uploads/2023/02/Depositphotos_132945206_S-300x200.jpg 300w, https://www.clouddatainsights.com/wp-content/uploads/2023/02/Depositphotos_132945206_S-768x512.jpg 768w, https://www.clouddatainsights.com/wp-content/uploads/2023/02/Depositphotos_132945206_S-930x620.jpg 930w" sizes="(max-width: 750px) 100vw, 750px" /><figcaption class="wp-element-caption"><em>Speedb moves towards open source in the quest for extremely fast data ingestion.</em></figcaption></figure></div>


<p><em>CloudDataInsights (CDI) caught up with Adi Gelvan,* Co-founder and CEO of Speedb, at a pivotal moment–It was about to re-launch itself as an open source company. The quest for a high-performing storage engine has led to open source and a new approach to understanding and solving common user challenges. There were many months of research, transitioning software engineers to a new mode of thinking and working, and building the community that will engage with the technology and collaborate in its evolution. This is the open-source quest for extremely fast data ingestion.</em></p>



<h5 class="wp-block-heading"><strong>CDI: Going all in on open source is a huge decision to make with lots of implications for your current customers and future users. How did you come to that decision?</strong></h5>


<div class="wp-block-image">
<figure class="alignright size-full is-resized"><img loading="lazy" decoding="async" src="https://www.clouddatainsights.com/wp-content/uploads/2023/03/Adi-Gelvan.png" alt="" class="wp-image-2440" width="325" height="395"/><figcaption class="wp-element-caption"><em>Adi Gelvan, Co-founder and CEO of Speedb</em></figcaption></figure></div>


<p><strong>Adi Gelvan: </strong>&nbsp;I think that being an open-source company really starts from being authentic. We had an interesting path to open source, and honestly, it wasn&#8217;t a business decision or a strategic decision. It was a path we went through. When we first incorporated the company, we were trying to find a solution to a very painful problem, and the path took us to open source.&nbsp;</p>



<p>We started the company around our cutting-edge technology. We had found a problem that thousands of companies are struggling with, that is, there was no scalable, high-performant, embedded key value store. The two market-leading technologies that addressed this gap were Level DB (open source but designed by Google for Google) and RocksDB (designed by Facebook for Facebook) are great, but they weren&#8217;t meant to be general-purpose solutions.</p>



<h5 class="wp-block-heading"><strong>CDI: So, the existing solutions addressed very specific data sets, data ecosystems, and use cases. That seems to contradict the open-source approach. Can you tell us more?</strong></h5>



<p><strong>Adi Gelvan: </strong>Yes, so they worked well for very specific workloads. They do a wonderful job for Facebook and Google and some companies like them. But thousands of customers are using them and not getting their merits because there are capabilities that are missing. When we came up with our hybrid compaction and the new technology that allowed us to develop Speedb which can outperform Level DB and RocksDB and give a lot of value to various workloads with large data sets. We said, &#8220;We’ve got it, now let&#8217;s start selling it.&#8221; Big companies were interested in our “secret sauce,” but in the end, companies are not the actual customers. It’s actually the person who presses Enter or writes the code to embed it–typically a developer. Well, developers don&#8217;t buy secret sauce even if it’s ten times better or a hundred times better.&nbsp;</p>



<p>There&#8217;s a saying in Hebrew that if three people are telling you that you&#8217;re a donkey, go find some grass to eat. Well,&nbsp; I needed about 300 people to tell me that, not just three.</p>



<p>Enough customers are saying, &#8220;I will buy it, just let me try it. Let me contribute. Let me actually help you do something that will fit my needs.&#8221; We said, &#8220;Okay.&#8221; If we had continued to work on our secret sauce behind closed doors, we might have done something wrong. That’s what led us to change not just the face of the company but the heart of the company.</p>



<p>Speedb developers, who hadn&#8217;t spoken to people in months, are now actively answering questions from the community on Discord. People whose code no one ever saw were now open-sourcing it.</p>



<h5 class="wp-block-heading"><strong>CDI: Engaging the community is probably also key to developing a versatile product, one that is agnostic because enterprise-driven development organizations often try to satisfy the biggest customer. The community can balance that influence and keep development moving in a generally acceptable direction. Are you seeing this balancing effect?</strong></h5>



<p><strong>Adi Gelvan: </strong>So I think that the essence of why we exist is because people who developed this technology were developing for the customer “du jour.” They did it for themselves. And if you see the biggest customers, by the way, the big giants who took RocksDB and forked it to their own needs, they also repeat stuff for their own needs. And you have giants like Alibaba and ByteDance, the mothership of TikTok. They have their own version and are doing their own stuff. But as they do their work in a silo, no one from the community is actually gaining from their work.</p>



<p>What we&#8217;re all about is <a href="https://www.clouddatainsights.com/prestocon-day-reveals-how-an-open-source-ecosystem-comes-together/">bringing value to the community</a>. And who is the community? These are developers who are excited about LSM and RocksDB but also want to bring value to their own mothership. They want some features that are not of interest to Facebook or ByteDance, but they have their own needs.&nbsp; In the RocksDB project, there were around 300 pull requests waiting for months to years for action. We&#8217;re gradually embedding these into the <a href="https://www.speedb.dev/">Speedb open-source code</a>. This is part of the community-building we are doing today.&nbsp;</p>



<p>Here is an Interesting example–we get more and more voices from the community, from bigger customers, like the biggest chip makers you can think of that are waiting for two years for two pull requests. We&#8217;re taking care of these right now, and we&#8217;re so glad that they are talking to us. That means that they also believe in what we&#8217;re doing. And I&#8217;m certain that a thousand engineers from the outside of a company know much more than the smartest people on earth if they&#8217;re on the inside, simply because the outside engineers are your target customers. They are the users.</p>



<h5 class="wp-block-heading"><strong>CDI: Now for a few technical questions. Why was including </strong><a href="https://www.rtinsights.com/mining-metadata-for-business-value-why-context-matters/"><strong>metadata </strong></a><strong>so important to you or to your customers?</strong></h5>



<p><strong>Adi Gelvan:</strong> Most of the data moving over networks today is metadata. The ratio between data and metadata in the past decade has dramatically changed because of connected devices and IoT.&nbsp;</p>



<p>For example, if you have a page of temperature readings then the metadata is the location, the height, etc. Sometimes it’s ten times the size of the temperature reading data. Now, if you can&#8217;t access the metadata, you can’t access the data. Legacy systems were simply not built to accommodate this volume of metadata.</p>



<h5 class="wp-block-heading"><strong>CDI: One of the techniques Speedb uses to handle large amounts of metadata is hybrid compaction. Can you explain what this is?</strong></h5>



<p><strong>Adi Gelvan: </strong>Compaction is a critical process within a data structure called log-structured merge-tree (LSM).&nbsp; The LSM tree has layers. Within the layers, you have sst files. When a level is filled with sst files, you join them together and write them to the next level as a bigger sst file. The data then takes less space when merged into one large file. You call this process compaction. Now, Google working with academia, invented the LSM tree. Facebook took it one step further, but everyone who tried to improve this mechanism was looking at it on an X and Y axis, so only in two dimensions. </p>



<p>Essentially what Speedb did was to look at the LSM tree in a multidimensional way and divided every level into multiple levels, which gave us another level of improvement of the compaction process. That’s the essence of hybrid compaction. Bottom line, the most important measurement of the efficiency of compaction is WAF = write amplification factor, which essentially means how many physical writes the system does compared to one logical write, and here we were able to improve it from X30 to X5.</p>



<h5 class="wp-block-heading"><strong>CDI: Tell us about the work you’ve done on the storage engine to support very fast writes.</strong></h5>



<p><strong>Adi Gelvan: </strong>The underlying engine of every database or application is the storage engine which takes the data that is written into the database and <a href="https://www.clouddatainsights.com/how-to-choose-cloud-storage-versus-data-center/">writes it into the underlying storage</a>, which can be media, flash, a file system, or the S3 protocol. They were often overlooked because they were very simple–all they did was handle the metadata and make sure the data was written in the right position. But today, when metadata started exploding and creating a real burden on every application, the storage engine suddenly became a bottleneck, a bottleneck that could not be addressed through old-generation key-value storage engines, SQL queries, or table structures.&nbsp;</p>



<p>Solving that bottleneck, often caused by metadata, is what Speedb does. It’s a very general use case since every system or every application that manages data is writing to a storage engine that does the work of distributing data onto the underlying media.</p>



<p>There is another bottleneck, which is, of course, the media you’re writing to. Speedb’s new hybrid compaction technology solves this problem in many cases.</p>



<h5 class="wp-block-heading"><strong>CDI: What’s next for Speedb?</strong></h5>



<p><strong>Adi Gelvan: </strong>The focus right now is to strengthen and expand the Speedb community. We are working on helping the community grow and increasing the amount of community-sourced code and enhancement requests. We find more and more companies that are facing performance and cost-efficiency challenges that are keen to work with us on solving their issues.</p>



<p><em>Find the Speedb open-source community at </em><a href="https://www.speedb.dev/">https://www.speedb.dev/</a>.<em> Find it on GitHub at</em> <a href="https://github.com/speedb-io/speedb"><em>https://github.com/speedb-io/speedb</em></a><em>.</em></p>



<p>* <em>Adi Gelvan is the Co-founder and CEO of <a href="http://speedb.io/">Speedb</a>. With over two decades of management, commercialization, and executive sales positions, Adi specializes in leading global software technology companies like Infinidat and SQream to outstanding growth. Adi holds a double academic degree in mathematics &amp; computer science. Adi invites everyone who is interested in Speedb to follow the project on <a href="https://github.com/speedb-io/speedb">GitHub</a> and give it a star if they like what they see.</em></p>
<div class="saboxplugin-wrap" itemtype="http://schema.org/Person" itemscope itemprop="author"><div class="saboxplugin-tab"><div class="saboxplugin-gravatar"><img alt='Elisabeth Strenger' src='https://secure.gravatar.com/avatar/d42bdc4339b8a684f54ad42d3ac0accb?s=100&#038;d=mm&#038;r=g' srcset='https://secure.gravatar.com/avatar/d42bdc4339b8a684f54ad42d3ac0accb?s=200&#038;d=mm&#038;r=g 2x' class='avatar avatar-100 photo' height='100' width='100' itemprop="image"/></div><div class="saboxplugin-authorname"><a href="https://www.clouddatainsights.com/author/estrenger/" class="vcard author" rel="author"><span class="fn">Elisabeth Strenger</span></a></div><div class="saboxplugin-desc"><div itemprop="description"><p>Elisabeth Strenger is a Senior Technology Writer at <a href="https://www.clouddatainsights.com/">CDInsights.ai</a>.</p>
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