Feb
23
2021
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Kleeen raises $3.8M to make front-end design for business applications easy

Building a front-end for business applications is often a matter of reinventing the wheel, but because every business’ needs are slightly different, it’s also hard to automate. Kleeen is the latest startup to attempt this, with a focus on building the user interface and experience for today’s data-centric applications. The service, which was founded by a team that previously ran a UI/UX studio in the Bay Area, uses a wizard-like interface to build the routine elements of the app and frees a company’s designers and developers to focus on the more custom elements of an application.

The company today announced that it has raised a $3.8 million seed round led by First Ray Venture Partners. Leslie Ventures, Silicon Valley Data Capital, WestWave Capital, Neotribe Ventures, AI Fund and a group of angel investors also participated in the round. Neotribe also led Kleeen’s $1.6 million pre-seed round, bringing the company’s total funding to $5.3 million.

Image Credits: Kleeen

After the startup he worked at sold, Kleeen co-founder, CPO and President Joshua Hailpern told me, he started his own B2B design studio, which focused on front-end design and engineering.

“What we ended up seeing was the same pattern that would happen over and over again,” he said. “We would go into a client, and they would be like: ‘we have the greatest idea ever. We want to do this, this, this and this.’ And they would tell us all these really cool things and we were: ‘hey, we want to be part of that.’ But then what we would end up doing was not that. Because when building products — there’s the showcase of the product and there’s all these parts that support that product that are necessary but you’re not going to win a deal because someone loved that config screen.”

The idea behind Kleeen is that you can essentially tell the system what you are trying to do and what the users need to be able to accomplish — because at the end of the day, there are some variations in what companies need from these basic building blocks, but not a ton. Kleeen can then generate this user interface and workflow for you — and generate the sample data to make this mock-up come to life.

Once that work is done, likely after a few iterations, Kleeen can generate React code, which development teams can then take and work with directly.

Image Credits: Kleeen

As Kleeen co-founder and CEO Matt Fox noted, the platform explicitly doesn’t want to be everything to everybody.

“In the no-code space, to say that you can build any app probably means that you’re not building any app very well if you’re just going to cover every use case. If someone wants to build a Bumble-style phone app where they swipe right and swipe left and find their next mate, we’re not the application platform for you. We’re focused on really data-intensive workflows.” He noted that Kleeen is at its best when developers use it to build applications that help a company analyze and monitor information and, crucially, take action on that information within the app. It’s this last part that also clearly sets it apart from a standard business intelligence platform.

Feb
18
2021
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Census raises $16M Series A to help companies put their data warehouses to work

Census, a startup that helps businesses sync their customer data from their data warehouses to their various business tools like Salesforce and Marketo, today announced that it has raised a $16 million Series A round led by Sequoia Capital. Other participants in this round include Andreessen Horowitz, which led the company’s $4.3 million seed round last year, as well as several notable angles, including Figma CEO Dylan Field, GitHub CTO Jason Warner, Notion COO Akshay Kothari and Rippling CEO Parker Conrad.

The company is part of a new crop of startups that are building on top of data warehouses. The general idea behind Census is to help businesses operationalize the data in their data warehouses, which was traditionally only used for analytics and reporting use cases. But as businesses realized that all the data they needed was already available in their data warehouses and that they could use that as a single source of truth without having to build additional integrations, an ecosystem of companies that operationalize this data started to form.

The company argues that the modern data stack, with data warehouses like Amazon Redshift, Google BigQuery and Snowflake at its core, offers all of the tools a business needs to extract and transform data (like Fivetran, dbt) and then visualize it (think Looker).

Tools like Census then essentially function as a new layer that sits between the data warehouse and the business tools that can help companies extract value from this data. With that, users can easily sync their product data into a marketing tool like Marketo or a CRM service like Salesforce, for example.

Image Credits: Census

Three years ago, we were the first to ask, ‘Why are we relying on a clumsy tangle of wires connecting every app when everything we need is already in the warehouse? What if you could leverage your data team to drive operations?’ When the data warehouse is connected to the rest of the business, the possibilities are limitless,” Census explains in today’s announcement. “When we launched, our focus was enabling product-led companies like Figma, Canva, and Notion to drive better marketing, sales, and customer success. Along the way, our customers have pulled Census into more and more scenarios, like auto-prioritizing support tickets in Zendesk, automating invoices in Netsuite, or even integrating with HR systems.

Census already integrates with dozens of different services and data tools and its customers include the likes of Clearbit, Figma, Fivetran, LogDNA, Loom and Notion.

Looking ahead, Census plans to use the new funding to launch new features like deeper data validation and a visual query experience. In addition, it also plans to launch code-based orchestration to make Census workflows versionable and make it easier to integrate them into an enterprise orchestration system.

Feb
17
2021
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TigerGraph raises $105M Series C for its enterprise graph database

TigerGraph, a well-funded enterprise startup that provides a graph database and analytics platform, today announced that it has raised a $105 million Series C funding round. The round was led by Tiger Global and brings the company’s total funding to over $170 million.

“TigerGraph is leading the paradigm shift in connecting and analyzing data via scalable and native graph technology with pre-connected entities versus the traditional way of joining large tables with rows and columns,” said TigerGraph founder and CEO, Yu Xu. “This funding will allow us to expand our offering and bring it to many more markets, enabling more customers to realize the benefits of graph analytics and AI.”

Current TigerGraph customers include the likes of Amgen, Citrix, Intuit, Jaguar Land Rover and UnitedHealth Group. Using a SQL-like query language (GSQL), these customers can use the company’s services to store and quickly query their graph databases. At the core of its offerings is the TigerGraphDB database and analytics platform, but the company also offers a hosted service, TigerGraph Cloud, with pay-as-you-go pricing, hosted either on AWS or Azure. With GraphStudio, the company also offers a graphical UI for creating data models and visually analyzing them.

The promise for the company’s database services is that they can scale to tens of terabytes of data with billions of edges. Its customers use the technology for a wide variety of use cases, including fraud detection, customer 360, IoT, AI and machine learning.

Like so many other companies in this space, TigerGraph is facing some tailwind thanks to the fact that many enterprises have accelerated their digital transformation projects during the pandemic.

“Over the last 12 months with the COVID-19 pandemic, companies have embraced digital transformation at a faster pace driving an urgent need to find new insights about their customers, products, services, and suppliers,” the company explains in today’s announcement. “Graph technology connects these domains from the relational databases, offering the opportunity to shrink development cycles for data preparation, improve data quality, identify new insights such as similarity patterns to deliver the next best action recommendation.”

Dec
16
2020
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Hightouch raises $2.1M to help businesses get more value from their data warehouses

Hightouch, a SaaS service that helps businesses sync their customer data across sales and marketing tools, is coming out of stealth and announcing a $2.1 million seed round. The round was led by Afore Capital and Slack Fund, with a number of angel investors also participating.

At its core, Hightouch, which participated in Y Combinator’s Summer 2019 batch, aims to solve the customer data integration problems that many businesses today face.

During their time at Segment, Hightouch co-founders Tejas Manohar and Josh Curl witnessed the rise of data warehouses like Snowflake, Google’s BigQuery and Amazon Redshift — that’s where a lot of Segment data ends up, after all. As businesses adopt data warehouses, they now have a central repository for all of their customer data. Typically, though, this information is then only used for analytics purposes. Together with former Bessemer Ventures investor Kashish Gupta, the team decided to see how they could innovate on top of this trend and help businesses activate all of this information.

hightouch founders

HighTouch co-founders Kashish Gupta, Josh Curl and Tejas Manohar.

“What we found is that, with all the customer data inside of the data warehouse, it doesn’t make sense for it to just be used for analytics purposes — it also makes sense for these operational purposes like serving different business teams with the data they need to run things like marketing campaigns — or in product personalization,” Manohar told me. “That’s the angle that we’ve taken with Hightouch. It stems from us seeing the explosive growth of the data warehouse space, both in terms of technology advancements as well as like accessibility and adoption. […] Our goal is to be seen as the company that makes the warehouse not just for analytics but for these operational use cases.”

It helps that all of the big data warehousing platforms have standardized on SQL as their query language — and because the warehousing services have already solved the problem of ingesting all of this data, Hightouch doesn’t have to worry about this part of the tech stack either. And as Curl added, Snowflake and its competitors never quite went beyond serving the analytics use case either.

Image Credits: Hightouch

As for the product itself, Hightouch lets users create SQL queries and then send that data to different destinations — maybe a CRM system like Salesforce or a marketing platform like Marketo — after transforming it to the format that the destination platform expects.

Expert users can write their own SQL queries for this, but the team also built a graphical interface to help non-developers create their own queries. The core audience, though, is data teams — and they, too, will likely see value in the graphical user interface because it will speed up their workflows as well. “We want to empower the business user to access whatever models and aggregation the data user has done in the warehouse,” Gupta explained.

The company is agnostic to how and where its users want to operationalize their data, but the most common use cases right now focus on B2C companies, where marketing teams often use the data, as well as sales teams at B2B companies.

Image Credits: Hightouch

“It feels like there’s an emerging category here of tooling that’s being built on top of a data warehouse natively, rather than being a standard SaaS tool where it is its own data store and then you manage a secondary data store,” Curl said. “We have a class of things here that connect to a data warehouse and make use of that data for operational purposes. There’s no industry term for that yet, but we really believe that that’s the future of where data engineering is going. It’s about building off this centralized platform like Snowflake, BigQuery and things like that.”

“Warehouse-native,” Manohar suggested as a potential name here. We’ll see if it sticks.

Hightouch originally raised its round after its participation in the Y Combinator demo day but decided not to disclose it until it felt like it had found the right product/market fit. Current customers include the likes of Retool, Proof, Stream and Abacus, in addition to a number of significantly larger companies the team isn’t able to name publicly.

Dec
03
2020
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Microsoft launches Azure Purview, its new data governance service

As businesses gather, store and analyze an ever-increasing amount of data, tools for helping them discover, catalog, track and manage how that data is shared are also becoming increasingly important. With Azure Purview, Microsoft is launching a new data governance service into public preview today that brings together all of these capabilities in a new data catalog with discovery and data governance features.

As Rohan Kumar, Microsoft’s corporate VP for Azure Data, told me, this has become a major pain point for enterprises. While they may be very excited about getting started with data-heavy technologies like predictive analytics, those companies’ data and privacy-focused executives are very concerned to make sure that the way the data is used is compliant or that the company has received the right permissions to use its customers’ data, for example.

In addition, companies also want to make sure that they can trust their data and know who has access to it and who made changes to it.

“[Purview] is a unified data governance platform which automates the discovery of data, cataloging of data, mapping of data, lineage tracking — with the intention of giving our customers a very good understanding of the breadth of the data estate that exists to begin with, and also to ensure that all these regulations that are there for compliance, like GDPR, CCPA, etc, are managed across an entire data estate in ways which enable you to make sure that they don’t violate any regulation,” Kumar explained.

At the core of Purview is its catalog that can pull in data from the usual suspects, like Azure’s various data and storage services, but also third-party data stores, including Amazon’s S3 storage service and on-premises SQL Server. Over time, the company will add support for more data sources.

Kumar described this process as a “multi-semester investment,” so the capabilities the company is rolling out today are only a small part of what’s on the overall road map already. With this first release today, the focus is on mapping a company’s data estate.

Image Credits: Microsoft

“Next [on the road map] is more of the governance policies,” Kumar said. “Imagine if you want to set things like ‘if there’s any PII data across any of my data stores, only this group of users has access to it.’ Today, setting up something like that is extremely complex and most likely you’ll get it wrong. That’ll be as simple as setting a policy inside of Purview.”

In addition to launching Purview, the Azure team also today launched into general availability Azure Synapse, Microsoft’s next-generation data warehousing and analytics service. The idea behind Synapse is to give enterprises — and their engineers and data scientists — a single platform that brings together data integration, warehousing and big data analytics.

“With Synapse, we have this one product that gives a completely no-code experience for data engineers, as an example, to build out these [data] pipelines and collaborate very seamlessly with the data scientists who are building out machine learning models, or the business analysts who build out reports for things like Power BI.”

Among Microsoft’s marquee customers for the service, which Kumar described as one of the fastest-growing Azure services right now, are FedEx, Walgreens, Myntra and P&G.

“The insights we gain from continuous analysis help us optimize our network,” said Sriram Krishnasamy, senior vice president, strategic programs at FedEx Services. “So as FedEx moves critical high-value shipments across the globe, we can often predict whether that delivery will be disrupted by weather or traffic and remediate that disruption by routing the delivery from another location.”

Image Credits: Microsoft

Dec
01
2020
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AWS adds natural language search service for business intelligence from its data sets

When Amazon Web Services launched QuickSight, its business intelligence service, back in 2016 the company wanted to provide product information and customer information for business users — not just developers.

At the time, the natural language processing technologies available weren’t robust enough to give customers the tools to search databases effectively using queries in plain speech.

Now, as those technologies have matured, Amazon is coming back with a significant upgrade called QuickSight Q, which allows users to just ask a simple question and get the answers they need, according to Andy Jassy’s keynote at AWS re:Invent.

“We will provide natural language to provide what we think the key learning is,” said Jassy. “I don’t like that our users have to know which databases to access or where data is stored. I want them to be able to type into a search bar and get the answer to a natural language question.

That’s what QuickSight Q aims to do. It’s a direct challenge to a number of business intelligence startups and another instance of the way machine learning and natural language processing are changing business processes across multiple industries.

“The way Q works. Type in a question in natural language [like]… ‘Give me the trailing twelve month sales of product X?’… You get an answer in seconds. You don’t have to know tables or have to know data stores.”

It’s a compelling use case and gets at the way AWS is integrating machine learning to provide more no-code services to customers. “Customers didn’t hire us to do machine learning,” Jassy said. “They hired us to answer the questions.”

Nov
12
2020
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Databricks launches SQL Analytics

AI and data analytics company Databricks today announced the launch of SQL Analytics, a new service that makes it easier for data analysts to run their standard SQL queries directly on data lakes. And with that, enterprises can now easily connect their business intelligence tools like Tableau and Microsoft’s Power BI to these data repositories as well.

SQL Analytics will be available in public preview on November 18.

In many ways, SQL Analytics is the product Databricks has long been looking to build and that brings its concept of a “lake house” to life. It combines the performance of a data warehouse, where you store data after it has already been transformed and cleaned, with a data lake, where you store all of your data in its raw form. The data in the data lake, a concept that Databricks’ co-founder and CEO Ali Ghodsi has long championed, is typically only transformed when it gets used. That makes data lakes cheaper, but also a bit harder to handle for users.

Image Credits: Databricks

“We’ve been saying Unified Data Analytics, which means unify the data with the analytics. So data processing and analytics, those two should be merged. But no one picked that up,” Ghodsi told me. But “lake house” caught on as a term.

“Databricks has always offered data science, machine learning. We’ve talked about that for years. And with Spark, we provide the data processing capability. You can do [extract, transform, load]. That has always been possible. SQL Analytics enables you to now do the data warehousing workloads directly, and concretely, the business intelligence and reporting workloads, directly on the data lake.”

The general idea here is that with just one copy of the data, you can enable both traditional data analyst use cases (think BI) and the data science workloads (think AI) Databricks was already known for. Ideally, that makes both use cases cheaper and simpler.

The service sits on top of an optimized version of Databricks’ open-source Delta Lake storage layer to enable the service to quickly complete queries. In addition, Delta Lake also provides auto-scaling endpoints to keep the query latency consistent, even under high loads.

While data analysts can query these data sets directly, using standard SQL, the company also built a set of connectors to BI tools. Its BI partners include Tableau, Qlik, Looker and Thoughtspot, as well as ingest partners like Fivetran, Fishtown Analytics, Talend and Matillion.

Image Credits: Databricks

“Now more than ever, organizations need a data strategy that enables speed and agility to be adaptable,” said Francois Ajenstat, chief product officer at Tableau. “As organizations are rapidly moving their data to the cloud, we’re seeing growing interest in doing analytics on the data lake. The introduction of SQL Analytics delivers an entirely new experience for customers to tap into insights from massive volumes of data with the performance, reliability and scale they need.”

In a demo, Ghodsi showed me what the new SQL Analytics workspace looks like. It’s essentially a stripped-down version of the standard code-heavy experience with which Databricks users are familiar. Unsurprisingly, SQL Analytics provides a more graphical experience that focuses more on visualizations and not Python code.

While there are already some data analysts on the Databricks platform, this obviously opens up a large new market for the company — something that would surely bolster its plans for an IPO next year.

Nov
11
2020
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Mozart Data lands $4M seed to provide out-of-the-box data stack

Mozart Data founders Peter Fishman and Dan Silberman have been friends for over 20 years, working at various startups, and even launching a hot sauce company together along the way. As technologists, they saw companies building a data stack over and over. They decided to provide one for them and Mozart Data was born.

The company graduated from the Y Combinator Summer 2020 cohort in August and announced a $4 million seed round today led by Craft Ventures and Array Ventures with participation from Coelius Capital, Jigsaw VC, Signia VC, Taurus VC and various angel investors.

In spite of the detour into hot sauce, the two founders were mostly involved in data over the years and they formed strong opinions about what a data stack should look like. “We wanted to bring the same stack that we’ve been building at all these different startups, and make it available more broadly,” Fishman told TechCrunch.

They see a modern data stack as one that has different databases, SaaS tools and data sources. They pull it together, process it and make it ready for whatever business intelligence tool you use. “We do all of the parts before the BI tool. So we extract and load the data. We manage a data warehouse for you under the hood in Snowflake, and we provide a layer for you to do transformations,” he said.

The service is aimed mostly at technical people who know some SQL like data analysts, data scientists and sales and marketing operations. They founded the company earlier this year with their own money, and joined Y Combinator in June. Today, they have about a dozen customers and six employees. They expect to add 10-12 more in the next year.

Fishman says they have mostly hired from their networks, but have begun looking outward as they make their next hires with a goal of building a diverse company. In fact, they have made offers to several diverse candidates, who didn’t ultimately take the job, but he believes if you start looking at the top of the funnel, you will get good results. “I think if you spend a lot of energy in terms of top of funnel recruiting, you end up getting a good, diverse set at the bottom,” he said.

The company has been able to start from scratch in the midst of a pandemic and add employees and customers because the founders had a good network to pitch the product to, but they understand that moving forward they will have to move outside of that. They plan to use their experience as users to drive their message.

“I think talking about some of the whys and the rationale is our strategy for adding value to customers […], it’s about basically how would we set up a data stack if we were at this type of startup,” he said.

Nov
10
2020
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Explo snags $2.3M seed to help build customer-facing BI dashboards

Explo, a member of the Y Combinator Winter 2020 class, which is helping customers build customer-facing business intelligence dashboards, announced a $2.3 million seed round today. Investors included Amplo VC, Soma Capital and Y Combinator, along with several individual investors.

The company originally was looking at a way to simplify getting data ready for models or other applications, but as the founders spoke to customers, they saw a big need for a simple way to build dashboards backed by that data and quickly pivoted.

Explo CEO and co-founder Gary Lin says the company was able to leverage the core infrastructure, data engineering and production that it had built while at Y Combinator, but the new service they created is much different from the original idea.

“In terms of the UI and the output, we had to build out the ability for our end users to create dashboards, for them to embed the dashboards and for them to customize the styles on these dashboards, so that it looks and feels as though it was part of their own product,” Lin explained.

While the founders had been working on the original idea since last year, they didn’t actually make the pivot until September. They made the change because they were hearing this was really what customers needed more than the tool they had been building while at Y Combinator. In fact, Chen says that their YC mentors and investors have been highly supportive of the switch.

The company is just getting started with the four original co-founders — Lin, COO Andrew Chen, CTO Rohan Varma and product designer Carly Stanisic — but the plan is to use this money to beef up the engineering team with three to five new hires.

With a diverse founding team, the company wants to continue looking at diversity as it builds the company. “One of the biggest reasons that we think diversity is important is that it allows us to have a bigger perspective and a grander perspective on things. And honestly, it’s in environments where I have personally […] been involved where we’ve actually been able to create the best ideas was by having a larger perspective. And so we definitely are going to be as inclusive as possible and are definitely thinking about that as we hire,” Lin said.

As the company has grown up during the pandemic, the founding core is used to working remotely and the goal moving forward is to be a distributed company. “We will be a remote distributed company so we’re hiring people no matter where they are, which actually makes it a lot easier from a hiring perspective because we’re able to reach a much more diverse and large pool of applicants,” Lin said.

They are in the process of thinking about how they can build a culture as they bring in distributed employees. “I think the way that we’ve started to see it is that working distributed is not a reduced experience, but just a different one and we are thinking about different things like how we organize new people when they on-board, and maybe we can meet up as a team and have a retreat where we are located in the same place [when travel allows],” he said.

For now, they will remain remote as they take their first half-dozen customers and begin to build the company with the new investment.

Oct
28
2020
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MachEye raises $4.6M for its business intelligence platform

We’ve seen our fair share of business intelligence (BI) platforms that aim to make data analysis accessible to everybody in a company. Most of them are still fairly complicated, no matter what their marketing copy says. MachEye, which is launching its AI-powered BI platform today, is offering a new twist on this genre. In addition to its official launch, the company also today announced a previously unreported $4.6 seed funding round led by Canaan Partners with participation from WestWave Capital.

MachEye is not just what its founder and CEO Ramesh Panuganty calls a “low-prep, no-prep” BI platform, but it uses natural language processing to allow anybody to query data using natural language — and it can then automatically generate interactive data stories on the fly that put the answer into context. That’s quite a different approach from its more dashboard-centric competition.

Image Credits: MachEye

“I have seen the business intelligence problems in the past,” Panuganty said. “And I saw that Traditional BI, even though it has existed for 30 or 40 years, had this paradigm of ‘what you ask is what you get.’ So the business user asks for something, either in an email, on the phone or in person, and then he gets an answer to that question back. That essentially has these challenges of being dependent on the experts and there is a time that is lost to get the answers — and then there’s a lack of exploratory capabilities for the business user. and the bigger problem is that they don’t know what they don’t know.”

Panuganty’s background includes time at Sun Microsystems and Bell Labs, working on their operating systems before becoming an entrepreneur. He built three companies over the last 12 years or so. The first was a cloud management platform, Cloud360, which was acquired by Cognizant. The second was analytics company Drastin, which got acquired by Splunk in 2017, and the third was the AI-driven educational platform SelectQ, which Thinkster acquired this April. He also holds 15 patents related to machine learning, analytics and natural language processing.

Given that track record, it’s probably no surprise why VCs wanted to invest in his new startup, too. Panuganty tells me that when he met with Canaan Partners, he wasn’t really looking for an investment. He had already talked to the team while building SelectQ, but Canaan never got to make an investment because the company got acquired before it needed to raise more funding. But after an informal meeting that ended up lasting most of the day, he received an offer the next morning.

MachEye’s approach is definitely unique. “Generating audio-visuals on enterprise data, we are probably the only company that does it,” Panuganty said. But it’s important to note that it also offers all of the usual trappings of a BI service. If you really want dashboards, you can build those, and developers can use the company’s APIs to use their data elsewhere, too. The service can pull in data from most of the standard databases and data warehousing services, including AWS Redshift, Azure Synapse, Google BigQuery, Snowflake and Oracle. The company promises that it only takes 30 minutes from connecting a data source to being able to ask questions about that data.

Interestingly, MachEye’s pricing plan is per seat and doesn’t limit how much data you can query. There’s a free plan, but without the natural search and query capabilities, an $18/month/user plan that adds those capabilities and additional search features, but it takes the enterprise plan to get the audio narrations and other advanced features. The team is able to use this pricing model because it is able to quickly spin up the container infrastructure to answer a query and then immediately shut it down again — all within about two minutes.

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