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. …
One of the most beneficial ways to increase your chances of being selected for a data engineering position is to add well-documented portfolio projects to your resume.
It not only improves your skills but also provides a tangible final product you can discuss with your recruiter.
We previously shared an article that looked over 5 examples of data engineering projects you could take on. But what if you want to make your own?
Where should you start?
Often, finding the right dataset is the hardest part of putting together an effective project.
Young developers and programmers often have many questions about how to advance their careers. No matter your current level, it’s essential to understand that engineering is not just about being smart technically.
Coding and technical skills only get you so far.
If you can’t get buy-in for projects or drive coding standards (in a way that gets people on board), then its hard to drive impact at a company.
Being a senior engineer, data scientist or other similar senior technical roles require a lot of growth outside of watching another tech tutorial.
Now another thing I noticed is that different…
Data engineering start-ups, content, and job interviews seem to be on the rise.
Perhaps this is because data engineering is finally becoming cool?
But, maybe it’s because companies are realizing to do any form of data science work you need data engineers to process the data first.
Don’t take my word for it.
Dice’s 2020 tech jobs report cites data engineering as the fastest-growing field in 2020, increasing by a staggering 50%, while data science roles only increased by 10%. You can rest assured that the influx of data engineering jobs will not regress anytime soon.
Even with a lot…
Last year Snowflake had its IPO and it was the largest software IPO in history.
All for a company focused on storing data for data analytics.
Fivetran, a company focused on moving data into data warehouses like Snowflake is now valued at over a billion dollars.
Being invested into the data life-cycle is in and here to stay.
So for this article, we wanted to focus on a specific venture fund that has been around since 1965 and has invested in multiple companies in the dataosphere.
Greylock Partners is one of the oldest venture…
2021 is almost halfway over, and it seems like hundreds of millions of dollars have gone into investing in data, data startups, and machine learning.
In particular, funding has also shifted heavily from just focusing on the data science and machine learning space to the data engineering and data management space.
Of course, if you’re an AI-based data management company, then I am sure you will be rolling in funding.
But let’s look to see what other data experts have to say.
We asked people from various parts of the data world to provide their insights into what they see…
All signs point towards an auspicious future for data engineering.
Dice’s 2020 tech jobs report cites data engineering as the fastest-growing field in 2020, increasing by a staggering 50%, while data science roles only increased by 10%. You can rest assured that the influx of data engineering jobs will not regress anytime soon. To bolster this supposition, the International Data Group (IDG) predicts that the five-year compound growth rate (CAGR) of data utilization from 2021 to 2024 will outweigh the total data creation spanning the entirety of the last 30 years. …
Have you heard of the ELTs, reverse ETLs, serverless databases, data lakehouses, or MLOps?
It’s always been hard to keep up in the tech industry when it comes to what the new best practice or solution is.
However, in the last few years, we have seen a proliferation of new technologies that can make it very difficult to keep up with the solutions marketplace.
Not to mention the recent marketing pushes to coin these various terms can be jarring.
This has made me think a lot about what are the right tools for which job.
More importantly, how do we…
Software engineer vs. data engineer.
People outside these professions are often confused about the differences between these experts. They may think these are two different names for the same thing: a programming expert. Of course, the duties of each engineer type are different, but their roles are similar enough to confuse people who are in the industry as well.
If you are a new data engineer, you may be confused yourself. What exactly separates you from a software engineer? The answer is not a simple one. In fact, you can honestly say, “It depends on the circumstances.” …
Just how did a Romanian startup company manage to accumulate $60 million in funding?
FintechOS launched in 2017 and is one of several startups disrupting the financial and insurance industries. Traditional banking has found it hard to respond to smaller, more user-friendly offerings that appeal to a broad audience — particularly younger generations. FintechOS provides a low-code platform aimed at giving larger and more conventional banks and insurance providers a boost, helping them build analytics tools and new services more easily.