As 2020 comes to a close, we wanted to take a moment to reflect on all the changes in technology as well as look to see where things are going.
Whether you are looking at startups and their IPOs, improvements in technology, or you paid attention to Amazon re:Invent, we saw a year filled with companies continuing to try to push boundaries.
A personal favorite announcement from 2020 was AWS’s SageMaker Data Wrangler that is designed to speed up data preparation for machine learning and AI applications. …
Technology has been speeding forward through 2020. The impacts of Covid have only sped up the adoption of change for companies.
Companies small and big, start-ups and corporations have been pushed to innovate and improve processes.
For example, small, medium, and large companies are migrating to the cloud to ensure their staff can do work from anywhere. This has been a decent amount of the work I have taken on this year.
I have also noticed an interest in small and medium organizations for not only adoption of the cloud but also improved data…
The focus for this week is streaming data solutions and analytics.
Streaming data is far from new. Kafka and other solutions have existed well over a decade. However, most of these were often very complex to implement and weren’t always approachable.
With that in mind, several companies have tried to make data streaming easier.
Last year we watched as Snowflake sky-rocketed in value. Nearing nearly 100 Billion dollars in value, the start-up’s IPO was the largest software IPO in history as of its debut.
But at the end of the day.
Snowflake is a data warehouse and analytics company.
And it’s not the only database and analytics company that is currently pushing to be part of the data boom.
Two companies in the last few weeks have both raised upwards of 100 million dollars with billion-dollar valuations.
These companies are Dremio and Cockroach Labs.
Integrating data into strategy is proving to be a differentiator for businesses of all sizes. The clichéd term “Data-Driven” isn’t just for billion-dollar tech companies.
Even smaller companies are finding savings and new revenue opportunities left and right thanks to data.
However, this is all easier said than done.
Just pulling data from all your different data sources isn’t always sufficient. There are a lot of problems that can come up with developing your data strategy and products.
In this article, I will outline…
How do you become a data engineer?
Unlike some of the other technical roles that have degrees and, generally speaking, a defined path, data engineering is a little less straightforward. Many of us might had never even heard of data engineers when we were taking our college courses. Yet companies like Facebook, Amazon, PayPal, and Walmart all have data engineering roles open right now, and there are also plenty of startups looking for data engineers.
But how do you go from college student to data engineer? What degrees do data engineers have? How does one become a data engineer? What…
If you’re a small business or start-up, you’re probably reading articles about companies using data science, data analytics and machine learning to increase their profits and reduce their costs. In fact, McKinsey just came out with a study that found that the companies they survey could attribute 20% of their bottom line to AI implementations. All those trendy and hyped up words are proving to be effective for companies of all sizes.
As data consultants we have had the opportunity to help multiple clients in industries like healthcare, insurance and transportation realize similar gains and cost savings. …
In 2014 a group of Google researchers put out a paper titled Machine Learning: The High-Interest Credit Card of Technical Debt. This paper pointed out a growing problem that many companies might have been ignoring.
Using the framework of technical debt, we note that it is remarkably easy to incur massive ongoing maintenance costs at the system level when applying machine learning. [D. Sculley, Gary Holt, etc]
Another way the researchers put this in a follow-up presentation was that launching a rocket was easy, but ongoing operations afterwards was hard. Back then, the concept of DevOps was still coming into…
Companies of all sizes are looking into implementing data science and machine learning into their products, strategies, and reporting.
However, as companies start managing data science teams, they quickly realize there are a lot of challenges and inefficiencies that said teams face.
Although it’s been nearly a decade since the over-referenced “Data Scientist: The Sexiest Job of the 21st Century” article, there are still a lot of inefficiencies that slow data scientists down.
Data scientists still struggle to collaborate and communicate with their fellow peers across departments. Also, the explosion of data sources inside companies has only made it more…