Photo: Confluent
Confluent sold Wall Street on data in motion. Enterprises are next.

Welcome to Protocol | Enterprise, your comprehensive roundup of everything you need to know about the week in cloud and enterprise software. This Monday: Confluent has its big day, Salesforce's DEI chief retires, and rivals pounce on Facebook's CRM dreams.
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Snowflake's smash-hit IPO was validation that non-hyperscalers can thrive as standalone vendors for data analytics. Confluent's Thursday debut was more subdued, but the 25% first-day rise in its shares still sent a clear signal.
The message? Enterprise data strategies are poised to get more sophisticated. Confluent, which runs on Apache Kafka, touts its ability to let users constantly compile and analyze data from Twitter, mobile apps, website clicks and many other sources — what it calls "data in motion." It's part of a burgeoning field known broadly as real-time analytics, one that is already seeing notable interest from investors.
The rise of real-time analytics could have a major impact on the future of database architecture. Advocates of real-time analytics say it can process data in seconds. That's much faster than the data warehouses that companies have used for decades, and it opens up different real-world uses.
While a few minutes may not seem like a huge deal, it shows a growing divide within the industry. Customers need to decide whether they want to spend money on software for specific use cases versus a single platform that looks to meet every worker's demands.
These are not just internal shifts. The rise of real-time analytics could have an impact on providers like Snowflake and Tableau.
For now, competition and cooperation go hand in hand. Confluent has partnerships and go-to-market agreements with Snowflake and Google Cloud, among others. It was even voted one of Google Cloud's top partners.
History has shown that major shifts in technology can be a huge advantage for the upstarts. The data analytics industry seems to be proving that yet again.
— Joe Williams
Recently, Micron announced new memory and storage innovations across its portfolio based on its industry-leading 176-layer NAND and 1α (1-alpha) DRAM technology. But what does "1α" mean, and just how amazing is it?
Salesforce's equality head retired: Tony Prophet, who also led recruiting for the software giant, had been on medical leave for the past few months. His departure, which was reportedly announced earlier this month in a Slack message from HR chief Brent Hyder, comes after Salesforce's simmering DEI issues were thrust into the public spotlight when two Black female managers posted resignation letters on social media sites alleging a culture of microaggressions and inequitable treatment at Salesforce.
Wenig was on our recent list of 10 people defining the new database landscape. Read the whole list here.
What excites you the most about the future of the database industry?
I'm continually amazed by the seemingly endless creativity of the community. The novel applications that we see today are possible thanks to the explosion of open-source tools, online courses and tutorials, and the accessibility of data sets. This has democratized the practice of working with big data and leveled the playing field for everyone.
What's your biggest career mistake or learning lesson?
I once designed the wrong product; I was trying to develop a classifier for a new application, and I thought I had all the information I needed and knew what I was doing. I couldn't have been more wrong. I learned several key lessons about cross-team communication and scoping projects: focus on the business value over technical minutia; iterate frequently with stakeholders and ensure you have direct access to them to avoid playing broken telephone; and ask questions about the data and validate any key assumptions with SMEs.
What's your advice to younger technologists who want to build a career in this field?
Just do it! Don't be intimidated by the hype. It's an ever-changing field and no one is fully caught up with all the latest advancements, so don't feel like you're starting late or behind others. Just jump in and work on a project that interests you. It doesn't matter if you're using the latest and greatest tools. The important thing is that you're solving a problem that you're passionate about and you're learning along the way. I highly recommend checking out kaggle.com to find fun data sets, form a team with friends and enter competitions to motivate you in your data journey.
What's one piece of recommended reading that you think should be a requirement for those in the industry?
I highly recommend reading the Datasheets for Datasets paper by [Timnit] Gebru et al. Every company tries to extract value from their data, but it's more difficult when there is no documentation about the data, such as how the data was collected, which fields can be null, how the data should (and should not) be used, etc. By creating "datasheets" for your data, you'll improve the quality of your data and any downstream applications.
What's the biggest hurdle companies are going to face in becoming a data-driven enterprise?
A lot of people focus on the technical aspects, but aligning people and the processes are just as important. Often data teams in large corporations operate in silos, and thus their data also exists in silos. Breaking down these silos is critical to becoming a data-driven enterprise, as the right people need access to the right data at the right time to make the right decisions.
Recently, Micron announced new memory and storage innovations across its portfolio based on its industry-leading 176-layer NAND and 1α (1-alpha) DRAM technology. But what does "1α" mean, and just how amazing is it?
Thanks for reading — see you Thursday!
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