Personal blog of Yzmir Ramirez

DevOps continues to get a lot of attention as a wave of companies develop more sophisticated tools to help developers manage increasingly complex architectures and workloads. In the latest development, Databand — an AI-based observability platform for data pipelines, specifically to detect when something is going wrong with a datasource when an engineer is using a disparate set of data management tools — has closed a round of $14.5 million.

Josh Benamram, the CEO who co-founded the company with Victor Shafran and Evgeny Shulman, said that Databand plans include more hiring; to continue adding customers for its existing product; to expand the library of tools that it’s providing to users to cover an ever-increasing landscape of DevOps software, where it is a big supporter of open-source resources; as well as to invest in the next steps of its own commercial product. That will include more remediation once problems are identified: that is, in addition to identifying issues, engineers will be able to start automatically fixing them, too.

The Series A is being led by Accel with participation from Blumberg Capital, Lerer Hippeau, Ubiquity Ventures, Differential Ventures and Bessemer Venture Partners. Blumberg led the company’s seed round in 2018. It has now raised around $18.5 million and is not disclosing valuation.

The problem that Databand is solving is one that is getting more urgent and problematic by the day (as evidenced by this exponential yearly rise in zettabytes of data globally). And as data workloads continue to grow in size and use, they continue to become ever more complex.

On top of that, today there are a wide range of applications and platforms that a typical organization will use to manage source material, storage, usage and so on. That means when there are glitches in any one data source, it can be a challenge to identify where and what the issue can be. Doing so manually can be time-consuming, if not impossible.

“Our users were in a constant battle with ETL (extract transform load) logic,” said Benamram, who spoke to me from New York (the company is based both there and in Tel Aviv, and also has developers and operations in Kiev). “Users didn’t know how to organize their tools and systems to produce reliable data products.”

It is really hard to focus attention on failures, he said, when engineers are balancing analytics dashboards, how machine models are performing, and other demands on their time; and that’s before considering when and if a data supplier might have changed an API at some point, which might also throw the data source completely off.

And if you’ve ever been on the receiving end of that data, you know how frustrating (and perhaps more seriously, disastrous) bad data can be. Benamram said that it’s not uncommon for engineers to completely miss anomalies and for them to only have been brought to their attention by “CEO’s looking at their dashboards and suddenly thinking something is off.” Not a great scenario.

Databand’s approach is to use big data to better handle big data: it crunches various pieces of information, including pipeline metadata like logs, runtime info and data profiles, along with information from Airflow, Spark, Snowflake and other sources, and puts the resulting data into a single platform, to give engineers a single view of what’s happening and better see where bottlenecks or anomalies are appearing, and why.

There are a number of other companies building data observability tools — Splunk perhaps is one of the most obvious, but also smaller players like Thundra and Rivery. These companies might step further into the area that Databand has identified and is fixing, but for now Databand’s focus specifically on identifying and helping engineers fix anomalies has given it a strong profile and position.

Accel partner Seth Pierrepont said that Databand came to the VC’s attention in perhaps the best way it could: Accel needed a solution like it for its own internal work.

“Data pipeline observability is a challenge that our internal data team at Accel was struggling with. Even at our relatively small scale, we were having issues with the reliability of our data outputs on a weekly basis, and our team found Databand as a solution,” he said. “As companies in all industries seek to become more data driven, Databand delivers an essential product that ensures the reliable delivery of high-quality data for businesses. Josh, Victor and Evgeny have a wealth of experience in this area, and we’ve been impressed with their thoughtful and open approach to helping data engineers better manage their data pipelines with Databand.”

The company is also used by data teams from large Fortune 500 enterprises to smaller startups.

Hevo founders Manish Jethani and Sourabh Agarwal.

According to data pipeline startup Hevo, many small to medium-sized companies juggle more than 40 different applications to manage sales, marketing, finance, customer support and other operations. All of these applications are important sources of data that can be analyzed to improve a company’s performance. That data often remains separate, however, making it difficult for different teams to collaborate.

Hevo enables its clients’ employees to integrate data from more than 150 different sources, including enterprise software from Salesforce and Oracle, even if they don’t have any technical experience. The company announced today that it has raised an $8 million Series A round led by Singapore-based venture capital firm Qualgro and Lachy Groom, a former executive at payments company Stripe.

The round, which brings Hevo’s total raised so far to $12 million, also included participation from returning investors Chiratae Ventures and Sequoia Capital India’s early-stage startup program Surge. The company was first covered by TechCrunch when it raised seed funding in 2017.

Hevo’s Series A will be used to increase the number of integrations available on its platform, and hire sales and marketing teams in more countries, including the United States and Singapore. The company currently has clients in 16 markets, including the U.S., India, France, Australia and Hong Kong, and counts payments company Marqeta among its customers.

In a statement, Puneet Bysani, tech lead manager at Marqeta, said, “Hevo saved us many engineering hours, and our data teams could focus on creating meaningful KPIs that add value to Marqeta’s business. With Hevo’s pre-built connectors, we were able to get data from many sources into Redshift and Snowflake very quickly.”

Based in Bangalore and San Francisco, Hevo was founded in 2017 by chief executive officer Manish Jethani and chief technology officer Sourabh Agarwal. The two previously launched SpoonJoy, a food delivery startup that was acquired by Grofers, one of India’s largest online grocery delivery services, in 2015. Jethani and Agarwal spent a year working at Grofers before leaving to start Hevo.

Hevo originated in the challenges Jethani and Agarwal faced while developing tech for SpoonJoy’s order and delivery system.

“All of our team members would come to us and say, ‘hey, we want to look at these metrics,’ or we would ask our teams questions if something wasn’t working. Oftentimes, they would not have the data available to answer those questions,” Jethani told TechCrunch.

Then at Grofers, Jethani and Agarwal realized that even large companies face the same challenges. They decided to work on a solution to allow companies to quickly integrate data sources.

For example, a marketing team at an e-commerce company might have data about its advertising on social media platforms, and how much traffic campaigns bring to their website or app. But they might not have access to data about how many of those visitors actually make purchases, or if they become repeat customers. By building a data pipeline with Hevo, they can bring all that information together.

Hevo is designed to serve all sectors, including e-commerce, healthcare and finance. In order to use it, companies sign up for Hevo’s services on its website and employees enter their credentials for software supported by the platform. Then Hevo automatically extracts and organizes the data from those sources and prepares it for cloud-based data warehouses, such as Amazon Redshift and Snowflake. A user dashboard allows companies to customize integrations or hide sensitive data.

Hevo is among several “no code, low code” startups that have recently raised venture capital funding for building tools that enable non-developers to add features to their existing software. The founders say its most direct competitor is Fivetran, an Oakland, California-based company that also builds pipelines to move data to warehouses and prepare it for analysis.

Jethani said Hevo differentiates by “optimizing our product for non-technical users.”

“The number of companies who need to use data is very high and there is not enough talent available in the market. Even if it is available, it is very competitive and expensive to hire that engineering talent because big companies like Google and Amazon are also competing for the same talent,” he added. “So we felt that there has to be some democratization of who can use this technology.”

Hevo also focuses on integrating data in real time, which is especially important for companies that provide on-demand deliveries or services. During the COVID-19 pandemic, Jethani says e-commerce clients have used Hevo to manage an influx in orders as people under stay-at-home orders purchase more items online. Companies are also relying on Hevo to help organize and manage data as their employees continue to work remotely.

In a statement about the funding, Qualgro managing partner Heang Chhor said, “Hevo provides a truly innovative solution for extracting and transforming data across multiple data sources — in real time with full automation. This helps enterprises to fully capture the benefit of data flowing though the many databases and software they currently use. Hevo’s founders are the type of globally-minded entrepreneurs that we like to support.”

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