Enterprise Data Cloud Storage Software market 2020-2026 emerging industry trends focuses on growth factors by major players Dell, Hewlett Packard Enterprise, Amazon, IBM, etc Daily Research Photographer
“nigeria startups when:7d” – Google News
Demand Sage, a new startup from the founders of recently-acquired mobile analytics company Localytics, announced this morning that it has raised $ 3 million in seed funding led by Eniac Ventures and Underscore VC.
When I spoke to CEO Raj Aggarwal, CTO Henry Cipolla and CPO Randy Dailey back in February, they outlined a vision to make it easier for marketers to get the data and insights they need, initially by automatically generating Google Sheets reports using data from HubSpot.
More recently, Demand Sage has been expanding into sales data.
“From our solid base with marketers we noticed sales leaders pulling us in to help them too,” Aggarwal told me via. “We’ve been able to give them visibility they didn’t have, in areas such as where deals are getting stuck and which activities actually drive revenue. It makes sense since there is a ton of overlap between the sales and marketing functions, especially in SMBs. ”
Aggarwal also said that Demand Sage has expanded its product lineup beyond pre-built report templates by introducing a no-code “Report Builder,” and by testing out an insights tools that could, for example, help salespeople determine which deals need their attention.
In a statement, Vinayak Ranade, CEO of Demand Sage customer Drafted, said, “With every sales and marketing tool I’ve used, eventually you give up and export data to a spreadsheet to dig into the numbers,” whereas with Demand Sage, it’s “like having a Google Sheets power-user that automatically makes the spreadsheets that you really want to see.”
As for how the business has fared during the pandemic, Aggarwal said, “Demand has really jumped. Companies need more cost-effective solutions and greater flexibility as business models shift.”
Varada, a Tel Aviv-based big data query acceleration company, recently announced a $ 12M (approx €10M) Series A round of funding led by MizMaa Ventures, an early-stage VC venture capital ﬁrm, with participation by Gefen Capital.
The existing investors including Lightspeed, StageOne Ventures, and F2 Venture Capital also participated in the round. The funding comes as the Israeli startup is preparing to launch its data virtualisation platform in general availability.
What problem does Varada solve?
Right now, the data space is driven by two emerging technologies: data lake and data virtualisation. Notably, the data lake enables organisations to avoid extensive, pre-consumption ETLs (Extract, Transform, Load) and significantly cut down on time-to-market.
With data virtualisation, disparate data sources are unified and data consumers can now query any data from a single end-point. This eliminates the heavy IT operations burden of configuration, modeling, and movement of data. However, the current data virtualisation options are hamstrung by scaling limitations and this is the problem Varada aims to solve.
“Varada is solving the biggest headaches of data infrastructure teams while giving business units the tools to quickly and cost-effectively turn their priceless data assets into value for customers,” says Eran Vanounou, CEO of Varada. “I know this problem firsthand, as I was the CTO of LivePerson before joining Varada. When I met the founding team, I was blown away at their vision for how to solve one of the thorniest problems in big data. The platform we’re building enables revolutionary ease-of-use, fast time-to-market, and cost control. This round of Series A funding will accelerate the progress of our solution and allow us to quickly scale our plans to deliver the new standard for data virtualisation.”
“Zero data-ops” solution
According to Varada, it is building a platform to revolutionise data virtualisation by offering an agile, “zero data-ops” solution. The company’s patented indexing technology uses machine learning to accelerate relevant and high-priority queries automatically without any overhead to query processing or any data maintenance. As per the company, the indexing works transparently for users, and indexes are managed automatically by Varada’s proprietary cost-based optimiser.
Varada was founded by David Krakov, Roman Vainbrand, and Tal Ben Moshe, veterans of the Dell EMC XtremIO core team, and is dedicated to leveraging new architecture to take on the challenge of data and business agility. The company has headquarters in Tel Aviv, with U.S. offices in San Mateo, California.
Main image credits: Varada
The post Israeli startup Varada secures €10M funding to launch New ML-based data virtualisation platform appeared first on Silicon Canals .
Snowflake went public this week, and in a mark of the wider ecosystem that is evolving around data warehousing, a startup that has built a completely new concept for modelling warehoused data is announcing funding. Narrator — which uses an 11-column ordering model rather than standard star schema to organise data for modelling and analysis — has picked up a Series A round of $ 6.2 million, money that it plans to use to help it launch and build up users for a self-serve version of its product.
The funding is being led by Initialized Capital along with continued investment from Flybridge Capital Partners and Y Combinator — where the startup was in a 2019 cohort — as well as new investors including Paul Buchheit.
Narrative has been around for three years, but its first phase was based around providing modelling and analytics directly to companies as a consultancy, helping companies bring together disparate, structured data sources from marketing, CRM, support desks and internal databases to work as a unified whole. As consultants, using an earlier build of the tool that it’s now launching, the company’s CEO Ahmed Elsamadisi said he and others each juggled queries “for eight big companies singlehandedly,” while deep-dive analyses were done by another single person.
Having validated that it works, the new self-serve version aims to give data scientists and analysts a simplified way of ordering data so that queries, described as actionable analyses in a story-like format — or “Narratives“, as the company calls them — can be made across that data quickly — hours rather than weeks — and consistently. (You can see a demo of how it works below provided by the company’s head of data, Brittany Davis.)
(And the new data-as-a-service is also priced in SaaS tiers, with a free tier for the first 5 million rows of data, and a sliding scale of pricing after that based on data rows, user numbers, and Narratives in use.)
Elsamadisi, who co-founded the startup with Matt Star, Cedric Dussud, and Michael Nason, said that data analysts have long lived with the problems with star schema modelling (and by extension the related format of snowflake schema), which can be summed up as “layers of dependencies, lack of source of truth, numbers not matching, and endless maintenance” he said.
“At its core, when you have lots of tables built from lots of complex SQL, you end up with a growing house of cards requiring the need to constantly hire more people to help make sure it doesn’t collapse.”
It was while he was working as lead data scientist at WeWork — yes, he told me, maybe it wasn’t actually a tech company but it had “tech at its core” — that he had a breakthrough moment of realising how to restructure data to get around these issues.
Before that, things were tough on the data front. WeWork had 700 tables that his team was managing using a star schema approach, covering 85 systems and 13,000 objects. Data would include information on acquiring buildings, to the flows of customers through those buildings, how things would change and customers might churn, with marketing and activity on social networks, and so on, growing in line with the company’s own rapidly scaling empire. All of that meant a mess at the data end.
“Data analysts wouldn’t be able to do their jobs,” he said. “It turns out we could barely even answer basic questions about sales numbers. Nothing matched up, and everything took too long.”
The team had 45 people on it, but even so it ended up having to implement a hierarchy for answering questions, as there were so many and not enough time to dig through and answer them all. “And we had every data tool there was,” he added. “My team hated everything they did.”
The single-table column model that Narrator uses, he said, “had been theorised” in the past but hadn’t been figured out.
The spark, he said, was to think of data structured in the same way the we ask questions, where — as he described it — each piece of data can be bridged together and then also used to answer multiple questions.
“The main difference is we’re using a time-series table to replace all your data modelling,” Elsamadisi explained. “This is not a new idea, but it was always considered impossible. In short, we tackle the same problem as most data companies to make it easier to get the data you want but we are the only company that solves it by innovating on the lowest-level data modelling approach. Honestly, that is why our solution works so well. We rebuilt the foundation of data instead of trying to make a faulty foundation better.”
Narrator calls the composite table, which includes all of your data reformatted to fit in its 11-column structure, the Activity Stream.
Elsamadisi said using Narrator for the first time takes about 30 minutes, and about a month to learn to use it thoroughly. “But you’re not going back to SQL after that, it’s so much faster,” he added.
Narrator’s initial market has been providing services to other tech companies, and specifically startups, but the plan is to open it up to a much wider set of verticals. And in a move that might help with that, longer term, it also plans to open source some of its core components so that third parties can data products on top of the framework more quickly.
As for competitors, he says that it’s essentially the tools that he and other data scientists have always used, although “we’re going against a ‘best practice’ approach (star schema), not a company.” Airflow, DBT, Looker’s LookML, Chartio’s Visual SQL, Tableau Prep are all ways to create and enable the use of a traditional star schema, he added. “We’re similar to these companies — trying to make it as easy and efficient as possible to generate the tables you need for BI, reporting, and analysis — but those companies are limited by the traditional star schema approach.”
So far the proof has been in the data. Narrator says that companies average around 20 transformations (the unit used to answer questions) compared to hundreds in a star schema, and that those transformations average 22 lines compared to 1000+ lines in traditional modelling. For those that learn how to use it, the average time for generating a report or running some analysis is four minutes, compared to weeks in traditional data modelling.
“Narrator has the potential to set a new standard in data,” said Jen Wolf, Initialized Capital COO and partner and new Narrator board member, in a statement. “We were amazed to see the quality and speed with which Narrator delivered analyses using their product. We’re confident once the world experiences Narrator this will be how data analysis is taught moving forward.”
With enterprise IT forming the connective tissue that ensures companies operate efficiently, Experience Level Agreements (XLAs) have become an emerging trend, a few companies specialise in this sector. Working on this front, HappySignals, a Helsinki-based Employee Experience Management Platform for IT makes experienced data visible and understandable. This way, it enables enterprises to change their culture to be more open, data-driven, and outcome-focused.
Secures €4.7M Series A funding
Now, HappySignals has closed a €4.7M Series A funding led by Nauta Capital, along with participation from Vendep Capital. This funding takes the overall funding secured by the Finnish startup to €6.2M. The company will use this investment to strengthen its mission of helping enterprise IT lenders and scaling their presence in the US and Europe.
“We believe happiness and productivity are the keys to transforming business IT culture for the better,” says HappySignals CEO Sami Kallio. “We do this by giving enterprise IT leaders the employee experience data they need to make outcome-focused IT decisions that drive digital transformation.”
“In Nauta Capital, we’ve found the perfect partner to help us scale operations. Their strong track record of supporting B2B disruptors represents a wealth of experience that will ensure we make the right moves going forward. We are also extremely thankful for the continued support of Vendep Capital, who has been with us almost two years now,” he adds.
Focuses on employees’ happiness
HappySignals founded in 2014 Sami Aarnio, Pasi Nikkanen, and Sami Kallio makes employees happier and increases productivity by 26% on average. It measures the experience possessed by employees and gets high volumes of experience data and merges it with operational data. The experience data is shared with partners, vendors, and stakeholders in real-time and help clients reach their goals. HappySignals creates an experience-driven IT department and improves overall productivity.
Main image picture credits: HappySignals
Pure Storage, the public enterprise data storage company, today announced that it has acquired Portworx, a well-funded startup that provides a cloud-native storage and data-management platform based on Kubernetes, for $ 370 million in cash. This marks Pure Storage’s largest acquisition to date and shows how important this market for multi-cloud data services has become.
Current Portworx enterprise customers include the likes of Carrefour, Comcast, GE Digital, Kroger, Lufthansa, and T-Mobile. At the core of the service is its ability to help users migrate their data and create backups. It creates a storage layer that allows developers to then access that data, no matter where it resides.
“I’m tremendously proud of what we’ve built at Portworx: an unparalleled data services platform for customers running mission-critical applications in hybrid and multi-cloud environments,” said Portworx CEO Murli Thirumale. “The traction and growth we see in our business daily shows that containers and Kubernetes are fundamental to the next-generation application architecture and thus competitiveness. We are excited for the accelerated growth and customer impact we will be able to achieve as a part of Pure.”
When the company raised its Series C round last year, Thirumale told me that Portworx had expanded its customer base by over 100 percent and its bookings increased by 376 from 2018 to 2019.
“As forward-thinking enterprises adopt cloud native strategies to advance their business, we are thrilled to have the Portworx team and their groundbreaking technology joining us at Pure to expand our success in delivering multi-cloud data services for Kubernetes,” said Charles Giancarlo, Chairman and CEO of Pure Storage. “This acquisition marks a significant milestone in expanding our Modern Data Experience to cover traditional and cloud native applications alike.”
Varada, a Tel Aviv-based startup that focuses on making it easier for businesses to query data across services, today announced that it has raised a $ 12 million Series A round led by Israeli early-stage fund MizMaa Ventures, with participation by Gefen Capital.
“If you look at the storage aspect for big data, there’s always innovation, but we can put a lot of data in one place,” Varada CEO and co-founder Eran Vanounou told me. “But translating data into insight? It’s so hard. It’s costly. It’s slow. It’s complicated.”
That’s a lesson he learned during his time as CTO of LivePerson, which he described as a classic big data company. And just like at LivePerson, where the team had to reinvent the wheel to solve its data problems, again and again, every company — and not just the large enterprises — now struggles with managing their data and getting insights out of it, Vanounou argued.
The rest of the founding team, David Krakov, Roman Vainbrand and Tal Ben-Moshe, already had a lot of experience in dealing with these problems, too, with Ben-Moshe having served at the chief software architect of Dell EMC’s XtremIO flash array unit, for example. They built the system for indexing big data that’s at the core of Varada’s platform (with the open-source Presto SQL query engine being one of the other cornerstones).
Essentially, Varada embraces the idea of data lakes and enriches that with its indexing capabilities. And those indexing capabilities is where Varada’s smarts can be found. As Vanounou explained, the company is using a machine learning system to understand when users tend to run certain workloads, and then caches the data ahead of time, making the system far faster than its competitors.
“If you think about big organizations and think about the workloads and the queries, what happens during the morning time is different from evening time. What happened yesterday is not what happened today. What happened on a rainy day is not what happened on a shiny day. […] We listen to what’s going on and we optimize. We leverage the indexing technology. We index what is needed when it is needed.”
That helps speed up queries, but it also means less data has to be replicated, which also brings down the cost. As MizMaa’s Aaron Applbaum noted, since Varada is not a SaaS solution, the buyers still get all of the discounts from their cloud providers, too.
In addition, the system can allocate resources intelligently so that different users can tap into different amounts of bandwidth. You can tell it to give customers more bandwidth than your financial analysts, for example.
“Data is growing like crazy: in volume, in scale, in complexity, in who requires it and what the business intelligence uses are, what the API uses are,” Applbaum said when I asked him why he decided to invest. “And compute is getting slightly cheaper, but not really, and storage is getting cheaper. So if you can make the trade-off to store more stuff, and access things more intelligently, more quickly, more agile — that was the basis of our thesis, as long as you can do it without compromising performance.”
Varada, with its team of experienced executives, architects and engineers, ticked a lot of the company’s boxes in this regard, but he also noted that unlike some other Israeli startups, the team understood that it had to listen to customers and understand their needs, too.
“In Israel, you have a history — and it’s become less and less the case — but historically, there’s a joke that it’s ‘ready, fire, aim.’ You build a technology, you’ve got this beautiful thing and you’re like, ‘alright, we did it,’ but without listening to the needs of the customer,” he explained.
The Varada team is not afraid to compare itself to Snowflake, which at least at first glance seems to make similar promises. Vananou praised the company for opening up the data warehousing market and proving that people are willing to pay for good analytics. But he argues that Varada’s approach is fundamentally different.
“We embrace the data lake. So if you are Mr. Customer, your data is your data. We’re not going to take it, move it, copy it. This is your single source of truth,” he said. And in addition, the data can stay in the company’s virtual private cloud. He also argues that Varada isn’t so much focused on the business users but the technologists inside a company.
Following 20 months of growth across Europe, iGenius, the creator of crystal.ai, a virtual advisor for business data, announced its expansion into the US. This market expansion coincides with the beta launch of crystal 2.0. As the CEO moves to the US and opens a New York City office, the company will invite select companies…
Sorry if this has been asked before, I’m pretty new here. So far I’ve done my market and user research online, I used multiple source to confirm it’s credibility but are there better ways to validate whatever data I get from third party website is credible? Says this blog or some news site stated how many % of users are behaving in this way in a study, but how do I verify it?