United States
I help B2B cloud and software vendors tell interesting, technically-credible stories that educate and attract customers.
As a former technology journalist (ZDNet, Computerworld, Reuters, South China Morning Post) for 15+ years, I bring deep expertise from the enterprise IT space.
I have also worked in-house, including as a content marketing director for 5 years. So I understand the marketing needs of enterprise vendors, as well as their processes.
My forte is blogging, particularly thought leadership. Some of my pieces have generated half a million views and 1000+ comments.
I have also edited and written more than 20 ebooks, as well as numerous customer case studies, whitepapers, infographics, presentations, web pages, lead-generation emails, video scripts, and more.
My clients range from Fortune 100 to startups, and include: FileInvite (financial services cloud) Rockset (cloud analytics), Acceldata (cloud data management), Cloudticity (healthcare cloud), Egnyte (cloud content management), Samsung (enterprise mobility), Oracle Communications (networking), Imperva (cybersecurity), Pinterest (online advertising), and Rogue Wave Software (DevOps).
Contact me at [email protected]
Your customers' personally identifiable information (PII) and personally identifiable financial information (PIFI) is enormously valuable, which is why it is under constant threat. According to the website Privacy Affairs, credit card account details sell for an average of $120 on the Dark Web, while a scanned U.S. driver's license is worth $150.
Streaming data has become a requirement for enterprises to build and operate great data products, and Apache Kafka is far and away the standard for real-time streaming. While adopting Kafka is becoming essential, data teams have several options when it comes to how to deploy Kafka.
The Rubik's Cube was invented in 1974, the same year that IBM released the first relational database. The similarities don't end there. Since its original rise in the 1980s, the Rubik's Cube has become the world's most-popular puzzle toy. More than 400 million Rubik's Cubes have been sold in the last four decades.
Sending sensitive documents via email is familiar and convenient. However, convenience has many downsides, with the biggest being poor data security. If your staff still rely on emailing sensitive information and unencrypted documents, including those containing your customers' PII and PIFI (Personally Identifiable Financial Information), then you are taking huge risks.
This is the fourth post in a series by Rockset's CTO Dhruba Borthakur on Designing the Next Generation of Data Systems for Real-Time Analytics . We'll be publishing more posts in the series in the near future, so subscribe to our blog so you don't miss them!
Of all the pieces of today's modern data stack, none has risen to prominence as fast as the cloud data platform, Snowflake. While part of the wave of multi-tenant cloud databases that also include Databricks, Amazon Redshift, and Google BigQuery, Snowflake has been able to stand out with a particularly developer-winning formula of easy startup costs, low ops, and instant, near-infinite scalability.
I had the pleasure of recently hosting a data engineering expert discussion on a topic that I know many of you are wrestling with - when to deploy batch or streaming data in your organization's data stack.
When the Fitbit debuted in 2009, skeptics dismissed it as a glorified and not particularly accurate step counter. Thirteen years later, the Fitbit is recognized as one of the creators of the modern data-infused wellness culture, with the now-Google subsidiary credited with leveraging the power of statistics and positive feedback to promote healthier lifestyles for countless individuals.
It's never been harder to be a Chief Data Officer. On the one hand, demand for CDOs is higher than ever, with more than two-thirds of enterprises appointing a CDO, up from less than one in eight in 2012. On the other hand, job security is lacking, with the average CDO lasting just 2.5 years.
A guide for data engineers and chief data officers.
Once big data's poster child, Hadoop is still very much alive in 2022, providing mission-critical analytics for a sneaky-large number of Fortune 500 companies. While some have moved their Hadoop clusters to the cloud or migrated these workloads to cloud data warehouses like Snowflake or Databricks, many companies are still running Hadoop on-premises.
This is the third post in a series by Rockset's CTO Dhruba Borthakur on Designing the Next Generation of Data Systems for Real-Time Analytics . We'll be publishing more posts in the series in the near future, so subscribe to our blog so you don't miss them!
Thought Leadership Blogs
"Super Bowl LVI took place earlier this year, and I watched the game like many of you. But as the CEO of a real-time analytics platform provider, I have to confess I was more fascinated by the action off the field, says" Venkat Venkataramani, co-founder and CEO, Rockset.
There's no shortage of enterprises still running mission-critical analytics the old-school way: via Hadoop clusters and other on-premises databases and data warehouses. With the imminent end of Cloudera support for Hortonworks Data Platform (HDP) and Cloudera Data Hub (CDH), many Hadoop users are scrambling for a way to maintain their infrastructure so they can avoid a rushed, risk-filled migration.
Failure metrics have been used by IT administrators for decades to track the reliability and performance of their infrastructure, whether it be PC hardware, networks, or servers. After all, most experts agree that to manage something well, you need to measure it.
It's like eBay, but for your company's data sets.
In the corporate world, the employee shuffle never stops. But since the pandemic, it's taken on a different flavor.
Companies everywhere have upgraded, or are currently upgrading, to a modern data stack, deploying a cloud native event-streaming platform to capture a variety of real-time data sources. So why are their analytics still crawling through in batches instead of real time? It's probably because their analytics database lacks the features necessary to deliver data-driven decisions accurately in real time.
This is the fifth post in a series by Rockset's CTO and Co-founder Dhruba Borthakur on Designing the Next Generation of Data Systems for Real-Time Analytics . We'll be publishing more posts in the series in the near future, so subscribe to our blog so you don't miss them!
Companies are embracing the modern data stack today because of the promise of speed, agility and cost savings. And make no mistake: the modern data stack overwhelmingly delivers on these promises.
For many organizations, Apache Kafka is the key layer in their modern data stack. Uber calls the event streaming platform "the cornerstone" of its tech infrastructure, processing trillions of real-time messages and multiple petabytes of data daily with Kafka. So do Walmart, Twitter, Tencent and many others.
Hadoop brought Big Data to businesses, enabling cost-effective, high-performance analytics on massive datasets for the first time. At one point last decade, every Fortune 50 company had either deployed a petabyte-scale Hadoop cluster running the open-source technology, or was making plans to do so. Hadoop's heyday was brief.
Not your father's Oracle cluster, but better.
Who needs boxing when we have two heavyweights brawling for supremacy in the cloud data arena?
Ahhh, the good old days. When a database was a server, not serverless. When data was stored in structured tables, not free-for-all data lakes. And when calculating the return on your data investments was straightforward.
Fast analytics on fresh data is better than slow analytics on stale data. Fresh beats stale every time. Fast beats slow in every space.
Whether they like it or not, every company today is in a race. The finish line: to become a fully-fledged 21st-century data-driven business.
Customer Case Studies
At ConveYour, we provide automated sales training via the cloud. Our all-in-one SaaS platform brings a fresh approach to hiring and onboarding new sales recruits that maximizes training and retention. High sales staff churn is wasteful and bad for the bottom line. However, it can be minimized with personalized training that is delivered continuously in bite-sized portions.
Whatnot is a venture-backed e-commerce startup built for the streaming age. We've built a live video marketplace for collectors, fashion enthusiasts, and superfans that allows sellers to go live and sell anything they'd like through our video auction platform. Think eBay meets Twitch. Coveted collectibles were the first items on our livestream when we launched in 2020.
Zembula is a Portland, Oregon-based venture-backed startup that is breaking new ground in real-time customer personalization. Expanding Smart Banners to all kinds of promotional emails caused our traffic to explode 10x. We needed a lower-ops, cost-effective and scalable database to pave the way for our next 100x of growth.
The popular open-source messaging/streaming system, Apache Kafka, is a key enabler for some of the most data-driven and disruptive companies today. Uber, which processes trillions of messages and multiple petabytes of data each day with Kafka, calls it the "cornerstone of our technology stack."
No one knows for sure, but Netflix may have the largest data infrastructure in the world.
The American dairy industry is a mighty one. America's 32,000 dairy farmers not only produce the most milk in the world, they are also the most efficient, producing 23 thousand pounds of milk per cow per year - almost 20 times the weight of an average (1,200 pound) dairy cow.
Facebook was founded at the dawn of the big data era, so it's appropriate that the company is not only renowned for its proficiency at using big data to drive its business, but also as one of the biggest producers of data.
With 406 million active users and 180 million paying subscribers, Spotify remains the dominant music streaming service in large part because of its exhaustive analysis of user behavior in order to deliver the best, most-personalized experience for every listener.
Rockset was incredibly easy to get started. We were literally up and running within a few hours. - Jeremy Evans, Co-founder and CTO, Savvy At Savvy, we have a lot of responsibility when it comes to data. Our customers are online consumer brands such as Brilliant.org, Flex and Simple Habit.
PubMatic is one of the United States' largest advertising technology companies. Since 2006, the company has created a complex, but highly efficient data infrastructure with eight global data centers. The company is one of the industry's leaders in programmatic advertising innovation.
PCH International is a leading hardware manufacturer with global operations that requires ultra-fast analysis of huge volumes of streaming data. The existing data infrastructure built on MongoDB and DynamoDB couldn't support real-time querying of data. PCH initially considered data warehouses such as Snowflake and Redshift, but found them too costly for real-time analytics.
Seesaw Learning Inc. provides a leading online student learning platform used by more than 10 million K-12 teachers, students and family members in the U.S. every month. The San Francisco company has grown steadily since its founding in 2013, with its hosted service in use in 75% of American schools and in another 150 countries.
There are more than 65 million speech-impaired people worldwide of every age and in every social sphere. Historically, they are a vulnerable social group, found in special education institutions, rehab centers, hospitals and clinics, or their own homes. Every one of them needs rehabilitation, education, and help, in order to communicate their needs, emotions and ideas.
At Dimona, a leading Latin American apparel company founded 55 years ago in Brazil, our business is t-shirts.
JP Morgan Chase is one of the largest banks in the world, with nearly $130 billion in revenue in 2020. With 50,000 IT employees and an annual IT budget of $12 billion a year, the company invests heavily to ensure its technology gives it a competitive advantage.
LinkedIn is the epitome of the modern data-driven enterprise. Its massive global professional social network is powered by its cutting-edge use of analytics, supported by its massive investment in data engineering.
Growth of the Internet of Things (IoT) hasn't matched the hype due to numerous pain points.
I co-edited this ebook, with 20+ enterprise customer case stories.
It's one of the most-famous business expressions, so ubiquitous that it's still hotly-debated who said it first, and so popular it has several variations: "If you can't measure it, you can't manage it." "You can only manage what you can measure." "What you cannot measure, you cannot improve."
Growth of the Internet of Things (IoT) hasn't matched the hype due to numerous pain points: limited, unreliable network coverage, high connectivity, and device maintenance costs, and the uncertainty created by diverse, constantly-evolving cellular standards (
Minimalism isn't just cool because decluttering gurus like Marie Kondo say so. As the world gets ever more complicated, simplicity emerges as a rare and valuable commodity. So less becomes more, and 2 - 1 = 3. The need for simplicity is never stronger than in the business technology realm.
There's no question that bad data hurts the bottom line. Bad customer data costs companies six percent of their total sales, according to a UK Royal Mail survey. The UK Government's Data Quality Hub estimates organizations spend between 10% and 30% of their revenue tackling data quality.
Executive Bylines
The modern data stack emerged a decade ago, a direct response to the shortcomings of big data.
I recently had the good fortune to host a small-group discussion on personalization and recommendation systems with two technical experts with years of experience at FAANG and other web-scale companies. Raghavendra Prabhu (RVP) is Head of Engineering and Research at Covariant, a Series C startup building an universal AI platform for robotics starting in the logistics industry.
In Part I , Venkat Venkataramani, co-founder and CEO, Rockset, explored the evolution of operational intelligence/business observability, from NASA to the cloud to today's data-driven enterprises.
Fast analytics on fresh data is better than slow analytics on stale data. Fresh beats stale every time. Fast beats slow in every space. Time and time again, companies in a wide variety of industries have boosted revenue, increased productivity and cut costs by making the leap from batch analytics to real-time analytics.
Out of all the awfulness created by the COVID-19 global pandemic, a few unexpected silver linings have emerged. One of them is in the field of economics, which in the past year has quietly undergone a revolution, a revolution that mirrors one that is happening in the business world.
To deliver real-time analytics, companies need a modern technology infrastructure that includes these three things:
Minimalism isn’t just cool because decluttering gurus like Marie Kondo say so. As the world gets ever more complicated, simplicity emerges as a rare and valuable commodity. So less becomes more, and 2 - 1 = 3.
The upheaval caused by the global pandemic in 2021 accelerated many changes in society, business, and technology. This was incredibly true in the sphere I work in, the cutting edge of data analytics.
Minimalism isn't just cool because decluttering gurus like Marie Kondo say so. As the world gets ever more complicated, simplicity emerges as a rare and valuable commodity. So less becomes more, and 2 - 1 = 3. The need for simplicity is never stronger than in the business technology realm.
Service Level Agreements (SLA) are the root canals of the business technology world. Everyone dreads them - the dragged-out meetings, hard-nosed negotiations, and endless SLA contract revisions. Yet, for organizations that have made major enterprise investments in the cloud, SLAs are required for any area where mission-critical business operations are at the mercy of a technology service.
Who's that dark shadow at your office door? Is it your (un)friendly neighborhood data engineer, looking like the world has come to an end? And when you suggest a different data set, do they sigh deeply and reply with a look of confusion and bewilderment? Look, you are not alone.
The traditional data catalog is going the way of the traditional data warehouse. Both are useful but legacy technologies. They still have roles in the modern data stack of today's digital enterprises - they just happen to be limited ones. A new technical approach, data discovery, is replacing the traditional data catalog.
The modern data stack emerged a decade ago, a direct response to the shortcomings of big data. Companies that undertook big data projects ran headlong into the high cost, rigidity and complexity of managing complex on-premises data stacks. Lifting and shifting their big data environment into the cloud only made things more complex.
I have ghostwritten many executive blogs, including this one for the BlackBerry CEO that has been read by 500,000 LinkedIn readers, generated 3,300 social reactions and 1000+ comments. Several other blogs have generated 100K views and nearly 1000 comments.
One of the key benefits of digital transformation is the democratization of data - who has access to it, who can analyze it, and who holds the resulting knowledge for influencing vital processes and decisions inside a modern business?
An Explainer Guide for Developers Building Data-Driven Applications
Your company’s mission-critical data is a lot like Schrodinger’s Cat. It’s trapped in a metaphorical black box into which you have very little visibility.
It’s football season, so here’s a question: what if we overhauled the rules? Instead of the final score, what if the winner was the team that gained more yards on offense. Or, erm, snapped the football to the QB more times? Sounds crazy -- yet, in a way this is how the success of a company’s data operations is measured today.
Data scientists used to be the company nerds. But data scientists — or data analysts, as well as their slightly older siblings, Business Intelligence (BI) analysts — have “glowed up.” Today, data scientists and analysts are heroes and MVPs.
Sometimes the best defense is a good offense. That’s never truer than when it comes to data quality and reliability.
Thought leadership blog for data performance management vendor, Acceldata.
Ghostwritten thought leadership blog on data privacy.
Ghostwritten thought leadership blog for the CEO.
For 3 years, I wrote a thrice-weekly blog about enterprise mobility sponsored by SAP. It generated 100,000 views per month and won top corporate blog from B2B Magazine.
I was chief editor for 4 years, leading the transition from smartphones to mobile security. During my time, we averaged one million readers per month.
This was among several dozen cybersecurity news blogs that I wrote or edited for leading cybersecurity vendor, Imperva.
Among 40+ blogs I have written for Samsung's B2B division, including this thought leadership blog (and infographic).
As content marketing director for Avaya, our blog readership nearly 300% Y/Y. We also grew our social engagement 125% Y/Y and social following 81% Y/Y.
Ebooks
data observability whitepaper
I launched and edited six issues of this magazine, which was sent to 10,000 existing and prospective customers.
One of half-a-dozen thought leadership ebooks I edited and co-wrote for BlackBerry.
Among 40+ blogs I have written for Samsung's business mobility division, this one helping promote its Galaxy S10 launch.
One of half-a-dozen thought leadership ebooks I edited for Avaya.
I co-wrote and edited this ebook on the potential of mobile devices for businesses.
As content marketing director, I edited and produced nearly ten thought leadership eBooks for BlackBerry, including this popular resource for CSOs.
This was a timely ebook, published when COVID forced workers to stay at home. It was a popular asset for Egnyte, a leading cloud content management provider for businesses.
Infographics
B2B thought leadership doesn't have to be dull and techie. This infographic in 14 slides (what I like to call 'infodeck'; alas this has not yet caught on) has a strong, credible POV and was a popular piece for mobile security vendor, BlackBerry.
My team produced this creative infographic and poster highlighting the communication headaches of the modern worker.
My content team created this 'info-deck' highlighting the 2014 Sochi Olympics for which Avaya was the chief telecom equipment supplier.
Cost optimization ebook
As content marketing director for Avaya, this was among half a dozen thought leadership ebooks I edited and produced. We won Silver Medal for Best Overall Editorial at the Content Marketing Awards.
Whitepapers and Product Briefs
Popular whitepaper promoting Samsung's mobile business solutions.
I wrote this eBook aimed at developers and DevOps managers promoting a leading developer tooling platform.
Solution brief on managed healthcare cloud.
Whitepaper for developers and DevOps managers
Social, Email and Other
I managed the social channels for Oracle's market-leading networking and security division.
One of many lead generation emails I wrote for Egnyte.