#hire databricks developers​

[noahssmith123]

Hire Databricks developers to build scalable data pipelines, optimize Spark performance, and integrate AI/ML solutions.

[infernovm]

Databricks to infuse $250M to double its R&D staff in India this year

Databricks is planning to double its research and development (R&D) staff in India by the end of this year in an effort to accelerate the development of new capabilities and large language models (LLMs).  “This year, we plan to hire an additional 100-plus R&D engineers to strengthen our capabilities,” Vinod Marur, senior vice president of Engineering at Databricks, said during a media…

[usm1566]

Top Artificial Intelligence Development Companies in USA

As artificial intelligence becomes a growing force in business, today's top AI companies are at the forefront of this emerging technology. AI companies combine multiple technologies to meet and exceed the expectations of use cases in the home, workplace and greater community, often leveraging cloud computing and edge computing.

Machine learning is a leader in this field, but today's leading AI organizations are expanding their technology reach through other technology categories and activities, from predictive analytics to business intelligence to data warehouse tools to deep learning, alleviating many industrial and personal pain points.

It is not limited to those companies, but every business is in plans to modernize with AI.

USM Business Systems

USM is a global leading Artificial intelligence development company in Frisco, United States. With our specialization using artificial intelligence and machine learning technologies, we enhance our client's business with evolving AI solutions.

Our AI experts aim to develop and deliver result oriented and customer centric AI-based mobility solutions for various businesses across diverse industries. We primarily provide AI services to the banking and finance, healthcare, manufacturing, retail, e-commerce, telecom, marketing and sales and education sectors.

We have earned a strong brand name as the best Artificial Intelligence (AI) services and solutions provider in Virginia, California, New York, Illinois, Texas, Florida, Washington and other states of the United States. 

Top services

AI Services and Solutions

Mobile application development

Workforce Management

Cloud Management

HR management

Machine Learning

Chatbot App Development

ValueCoders 

ValueCoders is a top CMMI level 3 certified IT company with more than 2500+ satisfied clients with 90+ positive reviews, making it the number one company in the USA. The company has 14+ years of experience with 700+ dynamic and skilled developers who have worked for major software development companies, startups and digital agencies.

If you are looking for the best machine learning and AI application Development Company in Frisco, ValueCoders is what you are looking for.

Top services

Blockchain development

Digital transformation

Staff augmentation

Software product development

Cross platform development

Application management

Databricks 

The company is based in San Francisco, US, with offices in various parts of the world. Databricks provides a simplified AI platform to seamlessly integrate data, analytics and business workloads.

An open cloud platform is built to integrate data warehouses and data lakes to help create a cost-effective and flexible solution for businesses. The organization experienced-

Data engineers to build successful data pipelines,

Data leaders to work on data + AI transformation

Data scientists to collaborate and develop data science models

10Pearls

10Pearls10Pearls is a software company, founded in 2004, focusing on areas such as mobile apps, web based apps, enterprise solutions. Fuel business acceleration through digital delivers highly satisfying customer experiences that take business to the next level. 

If you're looking for a company that strives to maximize efficiency, minimize surprises, and increase speed to market, 10pearls is the company you should hire today.

Top services

Mobile App Development

Devops

Cyber ​​security

Internet of Things

artificial intelligence

Team development

Emergency technology

C3.ai

Headquartered and : Headquartered in Redwood City, California, U.S., C3.ai has offices in Chicago, Houston, New York City, Redwood City, and Washington, D.C., among other American cities. Offices are present. Additionally, it maintains eight other countries' international offices.

Top services

It provides an end-to-end AI application development platform with a model-based architecture.

It offers data scientists a no-code AI development platform, C3 AI Ex Machina.

Read More: Artificial intelligence development companies in new York

Blue Label Labs

At Blue Labs, they understand the client's requirements from the beginning and help in their initial app design and development through app marketing to post-launch maintenance.

Top services

App development

App Marketing & PR offer

App Development Portfolio

artificial intelligence

iOS development

Android Development

Dataiku

Dataiku was founded in 2013 with its HQ in New York, US. The company believes in organizing the use of data to achieve business objectives with ease. It provides centralized solutions for building, deploying and managing AI-based applications in the enterprise. 

Top services

Data preparation and visualization

ML and MLOps

DataApps and Analytical Apps

Architecture and enterprise-level collaborations

The end

Due to plenty of companies around it is a crucial part of hiring the perfect AI development company that can understand all your needs and demands so that it can benefit your business as you envision. 

Our team of developers will interpret the information and perform in-depth data analysis of your business. Our services include natural language processing in AI, reinforcement learning and predictive analytics that deliver focused edge business solutions.

Are you ready? Contact us at sales@usmsystems.com to get one of the most cost effective, reliable, powerful and robust AI development companies in the USA, Los Angeles, New York, San Francisco, California and major cities across USA.

[researchdive]

Know about Data Science Platform Market Influencing Factors by Top Companies

Over the last decade, data science has been rapidly progressing both as a technology and as a discipline. Best practices have been created by the leading businesses and it is now becoming part of the operational core for organizations. However, there is a need for a next step for product evolution in data science platform that supports and provides both business users an integrated solution for managing, building, and optimizing predictive models. Nowadays, data science platform is the most talked about topic in data science meet-ups, conferences, and top publications. According to a Research Dive analyst review, the concept of data science platform is not novel in the big data space but the need of data science platform in business is still unknown to many.

Download an Exlcusive PDF Sample of Data Science Platform Market@ https://www.researchdive.com/download-sample/77 Need for a Data Science Platform  1)    To Enable Better Teamwork with Data Scientists If the data scientists are solving the same problem in several ways, the productivity will decrease as it won’t deliver effectual value to the organization. One of the best solutions to ensure effective teamwork with data scientists is to provide them with a centralized flexible platform and the required set of tools to work upon. By using a data science platform, it ensures that all the contributions of the data scientists i.e. data models, data visualizations, and code libraries exist in a single shared reachable location. This helps data scientists to reuse the code, facilitate better discussion around research projects, and share best practices to make data science easily scalable and less resource exhaustive. 2)    Help Minimalize Engineering Effort With data science platforms, the data scientists get help in moving analytical models into production without any need of additional engineering effort or DevOps. For instance, if a company wants to build a product recommendation engine then the data scientist will require the efforts of a software engineer for testing, refining and integrating the data model before the users start seeing the product recommendations on the basis of their behavior. A data science platform makes sure that the data models are accessible behind an API so that the data scientists do not have to depend much on engineering efforts. 3)    Help to Offload a Number of Low Value Tasks The burden of data scientists is released with the help of data science platforms. The burden of low value tasks such as reproducing past results, configuring environments for non-technical users, running reports, and scheduling jobs is offloaded from data scientists. 4)    Facilitate Faster Research and Experimentation Data scientists do not have to deal with extra data management tasks, as data science platforms allow people to see what and how others are working on. Moreover, whenever there is a new hire in the data science team, the employee can quickly start working as it is easier to restore the work of the people who leave through a unified platform over various isolated tools.

Customize report as per your format and definition of Data Science Platform Market@ https://www.researchdive.com/request-for-customization/77 The Market Overview Currently, the global market for data science platform is progressing rapidly and is about to positively grow in the near future. According to the Research Dive report, the global data science platform market is projected to garner a revenue of $224.3 billion at a 31.1% CAGR from 2019 to 2026. This is majorly due to the growing adoption of analytical tools across the globe for learning the unobserved customer purchasing pattern. The key prominent players of the market are adopting several strategies such as product development along with many approaches such as collaborations and R&D activities to stand strong in the global market. The major players of the global data science market include Alphabet Inc. (Google), Databricks, Domino Data Lab, Inc., Civis Analytics, Dataiku, Cloudera, Inc., IBM Corporation, Anaconda, Inc., Microsoft Corporation., and Altair Engineering, Inc.

[noahssmith123]

Hire Databricks developers to build scalable data pipelines, optimize Spark performance, and integrate AI/ML solutions.

[bythebayio]

Jean-Yves Stephan - Apache Spark Performance Tuning Session with Delight.

Jean-Yves Stephan is a co-founder at Data Mechanics, a Cloud-Native Spark Platform - deployed on a Kubernetes cluster inside their customers' cloud account. Prior to Data Mechanics, he was a software engineer leading the Spark infrastructure team at Databricks. JY is passionate about making Spark 10x more developer-friendly and cost-effective.

In your work with technology, what is the task, situation, setup when you’re the happiest and in the flow?

I’m the co-founder of Data Mechanics, a cloud-native spark platform focused on data engineering use cases.

I’ve been working with Apache Spark for about 6 years now.

I’m happiest when I work with our customers around questions like how to scale their data pipelines and their organisation, all the while reducing their costs.

On the technical side this implies data architecture (storage, compute) questions as well as Spark performance tuning and helping data teams excel with a Docker-based, Kubernetes-powered workflow.

And on the human side it means working with usually passionate and clever individuals, and help them build cool use cases around Spark.

What was the most positive thing you realized, or that just happened to you, during the pandemic?

On the professional side, our startup was acquired by Spot by NetApp, and we’re now integrating Data Mechanics with Spot.io, which will gives our customers plenty of new exciting features around infrastructure optimisation, cost visualisations and controls, and enterprise-grade security and infrastructure control.

On the personal side, I got married, and we managed to get a big group of friends and families safely together despite the pandemic. It was magical!

Should we stay online, go all-in for in-person, or do a hybrid setup next year?  Which one do you personally prefer?

At Data Mechanics and at Spot.io, we’re a remote-first company. That means we hire people in different countries and cities.

We also have some offices - for example a Paris office where some of our team members (including me) enjoy the office life (typically 2 days per week as far as I’m concerned).

So we are big defenders of the remote and flexible workstyle. Even for team members who work mostly remotely, we have regular in-person events (for some of them every month, for some of them every quarter), as obviously nothing it’s important to create bonds by spending time physically together - sharing a meal, doing an in-person white board session, or an escape game.

We are happy to invite you to the 9th conference Scale By the Bay!

Format: Online Dates: 28th-29th of October 2021 Learn the schedule Register to attend Visit our website Join us on Twitter Watch the videos from the previous years for inspiration ;)

[armeniaitn]

CodeSignal secures $50M for its tech hiring platform

New Post has been published on https://armenia.in-the.news/technology/codesignal-secures-50m-for-its-tech-hiring-platform-78772-17-09-2021/

CodeSignal secures $50M for its tech hiring platform

September 17, 2021 – 13:24 AMT

PanARMENIAN.Net – In less than a year after raising $25 million in Series B funding, technical assessment company CodeSignal – created by Armenian engineers – announced a $50 million in Series C funding to offer new features for its platform that helps companies make data-driven hiring decisions to find and test engineering talent, TechCrunch reports.

Similar to attracting a big investor lead for its B round — Menlo Ventures — it has partnered with Index Ventures to lead the C round. Menlo participated again and was joined by Headline and A Capital. This round brings CodeSignal’s total fundraising to $87.5 million.

Co-founder and CEO Tigran Sloyan got the idea for the company from an experience his co-founder and friend Aram Shatakhtsyan had while trying to find an engineering job. Both from Armenia, the two went in different paths for college, with Shatakhtsyan staying in Armenia and Sloyan coming to the U.S. to study at MIT. He then went on to work at Google.

“As companies were recruiting myself and my classmates, Aram was trying to get his resume picked up, but wasn’t getting attention because of where he went to college, even though he was the greatest programmer I had ever known,” Sloyan said. “Hiring talent is the No. 1 problem companies say they have, but here was the best engineer, and no one would bring him in.”

They, along with Sophia Baik, started CodeSignal in 2015 to act as a self-driving interview platform that directly measures skills regardless of a person’s background. Like people needing to take a driver’s test in order to get a license, Sloyan calls the company’s technical assessment technology a “flight simulator for developers,” that gives candidates a simulated evaluation of their skills and comes back with a score and highlighted strengths.

The need by companies to hire engineers has led to CodeSignal growing 3.5 times in revenue year over year and to gather a customer list that includes Brex, Databricks, Facebook, Instacart, Robinhood, Upwork and Zoom.

The new funding enabled the company to launch its Integrated Development Environment for candidates to interact with relevant assessment experiences like codes, files and a terminal on a machine that is familiar with them, so that they can showcase their skills, while also being able to preview their application. At the same time, employers are able to assign each candidate the same coding task based on the open position.

In addition, Sloyan intends to triple the company’s headcount over the next couple of months and expand into other use cases for skills assessment.

U.S. Department of Labor statistics estimate there is already a global talent labor shortage of 40 million workers, and that number will grow to over 85 million by 2030.

Read original article here

[jobsine]

Azure Databricks Application Engineer Job For 6-8 Year Exp In IBS Group Bengaluru / Bangalore, India - 3869966

Azure Databricks Application Engineer Job For 6-8 Year Exp In IBS Group Bengaluru / Bangalore, India – 3869966

Project DescriptionA leader in supermarkets and e-commerce, and a company at the forefront of sustainable retailing want to hire Azure professionals for their ongoing analytics workResponsibilitiesSupport 24/7Defines, designs, develops and test software components/applications using Microsoft Azure- Databricks, ADF, ADL, Hive, Python, Databricks, SparkSql, PySpark.Expert in Azure Data Bricks,…

[armeniaitn]

Armenian CodeSignal company secures $50 for its tech hiring platform

New Post has been published on https://armenia.in-the.news/economy/armenian-codesignal-company-secures-50-for-its-tech-hiring-platform-78756-16-09-2021/

Armenian CodeSignal company secures $50 for its tech hiring platform

In less than a year after raising $25 million in Series B funding, technical assessment company CodeSignal announced a $50 million in Series C funding to offer new features for its platform that helps companies make data-driven hiring decisions to find and test engineering talent, TechCrunch reports.

Similar to attracting a big investor lead for its B round — Menlo Ventures — it has partnered with Index Ventures to lead the C round. Menlo participated again and was joined by Headline and A Capital. This round brings CodeSignal’s total fundraising to $87.5 million.

Co-founder and CEO Tigran Sloyan got the idea for the company from an experience his co-founder and friend Aram Shatakhtsyan had while trying to find an engineering job. Both from Armenia, the two went in different paths for college, with Shatakhtsyan staying in Armenia and Sloyan leaving for the U.S. to study at MIT. He then went on to work at Google.

“As companies were recruiting myself and my classmates, Aram was trying to get his resume picked up, but wasn’t getting attention because of where he went to college, even though he was the greatest programmer I had ever known,” Sloyan told TechCrunch. “Hiring talent is the No. 1 problem companies say they have, but here was the best engineer, and no one would bring him in.”

They, along with Sophia Baik, started CodeSignal in 2015 to act as a self-driving interview platform that directly measures skills regardless of a person’s background. Like people needing to take a driver’s test in order to get a license, Sloyan calls the company’s technical assessment technology a “flight simulator for developers,” that gives candidates a simulated evaluation of their skills and comes back with a score and highlighted strengths.

The need by companies to hire engineers has led to CodeSignal growing 3.5 times in revenue year over year and to gather a customer list that includes Brex, Databricks, Facebook, Instacart, Robinhood, Upwork and Zoom.

The new funding enabled the company to launch its Integrated Development Environment for candidates to interact with relevant assessment experiences like codes, files and a terminal on a machine that is familiar with them, so that they can showcase their skills, while also being able to preview their application. At the same time, employers are able to assign each candidate the same coding task based on the open position.

In addition, Sloyan intends to triple the company’s headcount over the next couple of months and expand into other use cases for skills assessment.

Read original article here.

[magzoso-tech]

New Post has been published on https://magzoso.com/tech/starburst-raises-22m-to-modernize-data-analytics-with-presto/

Starburst raises $22M to modernize data analytics with Presto

Starburst, the company that’s looking to monetize the open-source Presto distributed query engine, today announced that it has raised a $22 million funding round led by Index Ventures, with the firm’s partner Mike Volpi joining the board. The general idea behind Presto is to allow anybody to use the standard SQL query language to run interactive queries against a vast amount of data that can sit in a variety of sources.

Like so many other open-source companies, Starburst plans to monetize Presto, which was originally developed at Facebook and open-sourced in 2013, by adding a number of enterprise-centric features on top, with the obvious focus being security features like role-based access control, as well as connectors to enterprise systems like Teradata, Snowflake and DB2, and a management console where users can configure the cluster to auto-scale, for example.

The Starburst co-founders, Justin Borgman and Matt Fuller, previously sold to Teradata their “SQL-on-Hadoop” company (Hadapt). After their tenure at Teradata, they decided to focus on turning Presto into an enterprise-grade service, and, after a few years, they succeeded in hiring Presto founders Dain Sundstrom, Martin Traverso and David Phillips, as well.

“What makes Presto so interesting is that it allows you to do data warehouse analytics without the data warehouse,” Starburst CEO Borgman told me. “What I mean by that is that you can query data anywhere. You don’t have to load the data, you don’t have to transform the data, and you don’t have to prepare the data.”

With this, an analyst can then access data anywhere, using regular SQL queries, without having to worry about the underlying infrastructure that makes it all work.

Starburst CEO Justin Borgman

Starburst’s overall mission to unify all of these data sources may sound a bit familiar, and I’ve heard somewhat similar pitches from other companies as well, including the likes of Databricks. Borgman, however, argues, that Starburst’s target audience is quite different from that of other projects, which tend to sit on top of the Spark engine. “We see Spark as very complementary to Presto,” he said. “What I mean by that is, we really think that Spark is best for the data scientist who is training machine learning models and working with Python notebooks, and writing code in Scala. Sort of the AI use cases. We’re focused exclusively on SQL — and SQL is a language that caters to a much broader audience. Maybe it’s not the data scientist PhD, but it’s the business analyst, the guy who went to business school and is trying to create some charts to show what’s going on with sales.”

The company says it will use the new funding to build out its sales force and marketing team, which it doesn’t really have right now, and expand its engineering team. Like similar open-source companies, chances are Starburst will, sooner or later, offer Presto as a managed service, too, though Borgman wasn’t quite ready to talk about that yet.

“Index has a long history of backing open source companies and data infrastructure companies. Some of these have now become household name: MySQL, Elastic, Confluent, Datadog and Kong to name a few,” Index Venture’s Volpi writes in a bog post today. That already made Starburst a good fit for a potential investment, though he also notes that bringing the Presto founders on board helped seal the deal and something he helped engineer.

“Our great fortune was that Justin and Matt are immensely wise and able to put aside ego’s and short term personal gain,” writes Volpi. “We were excited when they came to terms with Dain, Martin, and Dave. The end result was a reborn Starburst — a company constituted of the entrepreneurs that seized the commercial opportunity of Presto and the genius founders who invented it in the first place.”

[actutrends]

2020: Our meatless, cashless, city-less future 

Happy New Year! 2019 has come and gone like Kylo Ren’s reign in The Rise of Skywalker, and so it’s time for my (13th!) annual prediction piece for VentureBeat. But first, a quick look at the scorecard for my 2019 predictions:

Facial recognition trials will launch in large public venues outside of China. 2019 saw numerous trials, including at carnivals in Nice, France, airports in Las Vegas, and public housing in Detroit. Of course bans of the technology has already occurred, too, in cities like San Francisco and Somerville, MA. Grade:  A

Enterprise analytics will become red hot. I predicted this space would reach a third of the value of the workflow platform space by the end of 2019. With Databricks hitting $6.2 billion in value, ThoughtSpot achieving a $2 billion valuation, and many others, I nailed this one. Grade:  A

Car companies will begin transforming into tech companies. I said we’d see “major automobile manufacturers actively acquire or sign new partnerships with various tech companies to avoid becoming irrelevant in the near future.” Hyundai’s $4 billion self-driving joint venture with Aptiv, Toyota’s $100 million fund for autonomous driving, and Daimler and BMW’s partnership to develop autonomous driving technologies are a sampling of the moves made in 2019. Grade:  A

Voice advertising will be stalled. Hear those ads?  (chirp, chirp). Grade:  A

Data scientists will become the new call center reps. 2019 saw headlines like “Data science dominates LinkedIn’s emerging jobs ranking,” but I believe this trend needs to ferment a bit more like a good batch of kimchi. It takes time to train and develop new waves of data scientists. According to LinkedIn, over the past three years data scientist jobs experienced a 37% hiring growth. I also know of a large pharmaceutical company that laid off over 1,000 research scientists and hired nearly 500 data scientists, but these type of hiring trends aren’t strong enough to call them the new “call center reps.” Grade:  B-

National cryptocurrencies. I also want to revise the grade on one of my 2018 predictions. I gave myself a F after my bet that China and five other OECD nations would launch their own cryptocurrencies in 2018 fell flat. But China did come through in 2019, so I’m going to bump my score up to a “B” on that one. I’m confident many other nations will quickly follow China’s move.

This coming year will be filled with more exciting advances and changes in the tech industry even with an economic downturn looming, so here are my predictions for 2020:

What we can look forward to in 2020

1. Cloud kitchens will become a multibillion dollar industry

Launching a new restaurant in the largest cities is riskier than launching a tech startup. The restaurant business is a $799 billion industry in the U.S. alone and is begging for efficiencies. “Cloud kitchens” — virtual restaurants that cater solely to delivery — have emerged as a solid trend. They eat up far less real estate costs than traditional restaurants and capitalize on the many new on-demand food-delivery companies, such as UberEats, Deliveroo, and their ilk. I see big opportunities within this space in 2020, with a couple of unicorns being formed and a few acquisitions taking place. (A cloud-kitchen startup that was in my accelerator program in Korea was recently snapped up in an acquisition just 6 months after our investment, despite us trying to convince the founder to ignore the offer.)

2. Gibson’s and Chicago Cut will serve Impossible Food’s meat substitute

To be honest, I’m not a fan yet of Impossible Foods or Beyond Meat, but my taste buds do not matter with this prediction. The real prediction is that these plant-based meat substitutes are becoming so pervasive that they will infiltrate two of my favorite steakhouses in the world. For 2020, I predict meat substitutes will penetrate over 10% of restaurants in the U.S. The drivers are climate change and the practical fact that these newer meat substitutes taste good to meat lovers.

3. 5G will sputter searching for the right services

Beyond the hype of 5G, there are limitations to the millimeter wave-based network part of 5G and many network build outs are behind in the U.S. Regardless, the most important aspect about 5G’s adoption is content and services. Games on phones and VR headsets (i.e. Oculus Quest, Hololens) with 5G chips are a given, but what else? There will be adoption, but not a mad rush in 2020 for 5G devices and services. The real rush won’t come until more services that fully utilize 5G appear.

4. We’ll see a global movement towards a cashless world

Even with all the talk about Alipay and WeChat Pay, China uses cash more than the U.S. (40% vs. 37%). South Korea is the lowest at 14%, but globally we are far from a cashless world. Even last year, when speaking at a conference in Berlin, I had to walk five blocks to find a store that accepted credit cards to buy a bottle of water.

But we will get there because the younger generation is driving this cashless world (i.e. Venmo), and the technology is finally good enough that people can easily forgo cash. This year will see various online payment systems (PayPal, Crypto, Apple Pay, Google Pay, etc.) pick up bigger adoption, shifting the average cash usage of the 10 countries listed here to below 45% (vs. 55%):

And beyond ….

Since we’re also at the start of a new decade, here are a few more predictions of what the world will look like by the time we hit the next decade in 2030:

1. Technology will bring back suburban development

Discussions of trends for denser urban environments are almost becoming like what diet has the most impact… Keto saved my life!  Plant-based is the way to go! Mediterranean diet is the game changer! (I just started the Potato diet, so do not send me pictures of ribs, bacon or filet mignon.)

So my prediction here is one more addition to the current mix of ideas of where urban development is trending. I believe that the development of autonomous vehicles (e.g. cars, buses, trucks), cloud kitchens, drone delivery, and other new technologies will shift the population trend and real estate development back towards suburbs and even to the outskirts of major cities over the next decade. The concept of commute time will become different with autonomous cars since people can sleep more, work, do home finances or whatever they please while in transit. A welcome outcome of this reverse mega-city trend would be the stabilization of or decrease in real estate prices, especially in cities such as San Francisco and New York.

2. Technology tribes will begin to coexist with nations

Facebook’s Libra scares the bejesus out of nations and threatens their sovereignty, so during 2020 they continue to fight like a cornered rat against its launch. But the very concept of Libra signals the beginning of something new: the development of technology tribes that exist outside of sovereign nations — or coexist with them. As technology becomes more ubiquitous and the digital divide closes, new generations will develop stronger affinities with specific tech communities than with ethnicity or race. The coming decade will show beginnings of this trend, but it will become really palpable 20 or 30 years out.

3. The next Mark Zuckerberg will be from outside Silicon Valley

Until recently the tech world has been divided into two groups: Silicon Valley and everyone else. But the world is becoming flatter now in terms of entrepreneurial knowledge and execution. The venture capital and investor quality along with Silicon Valley’s “pay it forward” culture will strengthen across other global startup ecosystems over the coming decade to the point where we will see the next Zuckerberg rise out of what we currently deem a second-tier startup city.

Bernard Moon is cofounder and Partner at SparkLabs Group, a network of accelerators and venture capital funds.

The post 2020: Our meatless, cashless, city-less future  appeared first on Actu Trends.

[beingpip]

Building a stellar Engineering organisation

Intro: Scott Beechuk, Partner at Norwest Venture Partners, brings together world-class SaaS engineering leaders Claire Hough, Vijay Gill and Weiping Peng for a dynamic conversation on where SaaS technology is headed, how to build top performing engineering teams and what it takes to lead in today’s high-velocity engineering environment. Claire Hough is the VP of Engineering at Apollo and has nearly 20 years leading engineering and development teams at Udemy, Napster, and Netscape. Vijay Gill is the head of engineering at DataBricks and brings engineering leadership experience from Google, Microsoft, AOL, and Salesforce. Weiping leads the engineering team as the top software architect for Salesforce Service Einstein, the company’s customer service AI platform.

Q: What does it take to hire and retain the best engineering talent against the big names in the industry and different locations.

A: Play to your strengths - play to differential scope: impact and power. There's a lot greater capacity for those at a smaller company. "Whether you join Google or not is not going to have impact at Google.

Distributed teams - find talent where you can and use technology to connect people. Dig into their motivation and goal - get them excited about your mission. The people who want to join Google and have that as their goal aren't the people you want.

Q: What's the biggest fail in dealing with a remote team?

A: So many! Two or three main failure modes. Never use the term remote as it makes it worse. Call them distributed. Dont give them crap work as you end up with crap people. They're not going to stay if they're not stupid. Productivity falls. Give them end to end accountability and autonomy - this is hard as you're not used to it. Start narrow, make sure the interfaces are small and the APIs are tight. Dont expect to save money, you'll spend it on flights.

Q: What is the 1 skill every engineering leader must have?

A: Stay on top of all the technology trends. Stay open to different ways of doing things. You cant build everything yourself. Be aware of who you might partner with and what open source ideas you might use. Understand the impact of your business unit.

Maintain a growth mindset and openness. Be constantly learning about not just technology but also your people and what motivates them. Be able to admit a mistake, reset and make progress.

You must be able to empathize with your people. Avoid an 'us vs them' approach.

Q: What are some of the best practices for how Engineering should work with Product and UX.

A: Always remember you are one machine. Left arm and right arm of one body. We're developing one product and whether it makes money, is usable and whether people love it, it takes all of us.

One PDE (Product-Design-Engineering) team - one status report. Might be disagreements, but the end goal is serving the customers. Let everyone bring their ideas to the table and let each drive the bit they are expert on. Bring the great talent together.

One example of a fundamental disagreement between Product and Engineering... New feature vs RAS (reliability, availability, serviceability.) Balancing the investment is hard. Joint PRDs (prod requirement documents) and capacity plan together. Customer lens is critical.

If you dont have tensions and people speaking up, that is a problem.

Q: How do you grow engineering leaders from new grads?

A: Train them in empathy. Help them deal with difficult situations. Expose them to a multitude of people problems, customer problems and technology problems.

Have job ladder with specific expectations and empowerment frameworks.

Mentorship. Pair them with great leaders - who they pair with is critical. Play to strengths. There will be failures so have a senior person to catch them when they fall.

Give them ownership, but if it is complex modularise it.

Q: What is the main ingredient in building a great product.

A: Startups are made of people with grit. It's hard and you've gotta have 150% in and tackle each problem that's hard and stay in it. There is always a solution that you can figure out.

Try to be lucky. Luck strictly dominates over every other thing. If you're not luck y then build a team with people who have the factors for success... Mission and vision, someone who's good at execution, at product and at people.

Every startup's default state is dead.

Trust the people!

[toomanysinks]

Databricks raises $250M at a $2.75B valuation for its analytics platform

Databricks, the company behind the Apache Spark big data analytics engine, today announced that it has raised a $250 million Series E round led by Andreessen Horowitz. Coatue Management, Microsoft and NEA, also participated in this round, which brings the company’s total funding to $498.5 million. Microsoft’s involvement here is probably a bit of a surprise, but it’s worth noting that it also worked with Databricks on the launch of Azure Databricks as a first-party service on the platform, something that’s still a rarity in the Azure cloud.

As Databricks also today announced, its annual recurring revenue now exceeds $100 million. The company didn’t share whether it’s cash flow-positive at this point, but Databricks CEO and co-founder Ali Ghodsi shared that the company’s valuation is now $2.75 billion.

Current customers, which the company says number around 2,000, include the likes of Nielsen, Hotels.com, Overstock, Bechtel, Shell and HP.

While Databricks is obviously known for its contributions to Apache Spark, the company itself monetizes that work by offering its Unified Analytics platform on top of it. This platform allows enterprises to build their data pipelines across data storage systems and prepare data sets for data scientists and engineers. To do this, Databricks offers shared notebooks and tools for building, managing and monitoring data pipelines, and then uses that data to build machine learning models, for example. Indeed, training and deploying these models is one of the company’s focus areas these days, which makes sense, given that this is one of the main use cases for big data, after all.

On top of that, Databricks also offers a fully managed service for hosting all of these tools.

“Databricks is the clear winner in the big data platform race,” said Ben Horowitz, co-founder and general partner at Andreessen Horowitz, in today’s announcement. “In addition, they have created a new category atop their world-beating Apache Spark platform called Unified Analytics that is growing even faster. As a result, we are thrilled to invest in this round.”

Ghodsi told me that Horowitz was also instrumental in getting the company to re-focus on growth. The company was already growing fast, of course, but Horowitz asked him why Databricks wasn’t growing faster. Unsurprisingly, given that it’s an enterprise company, that means aggressively hiring a larger sales force — and that’s costly. Hence the company’s need to raise at this point.

As Ghodsi told me, one of the areas the company wants to focus on is the Asia Pacific region, where overall cloud usage is growing fast. The other area the company is focusing on is support for more verticals like mass media and entertainment, federal agencies and fintech firms, which also comes with its own cost, given that the experts there don’t come cheap.

Ghodsi likes to call this “boring AI,” since it’s not as exciting as self-driving cars. In his view, though, the enterprise companies that don’t start using machine learning now will inevitably be left behind in the long run. “If you don’t get there, there’ll be no place for you in the next 20 years,” he said.

Engineering, of course, will also get a chunk of this new funding, with an emphasis on relatively new products like MLFlow and Delta, two tools Databricks recently developed and that make it easier to manage the life cycle of machine learning models and build the necessary data pipelines to feed them.

source https://techcrunch.com/2019/02/05/databricks-raises-250m-series-e-for-its-analytics-platform/