Hello lovely people!
Data Love is the conference for Data Engineers, Data Scientists.
- Wonder about new trends in the Data engineering world?
- Interested in what is happening in Data Science?
- Puzzled about how to meet both engineering and scientific requirements? Master real, in-demand tech skills from home with Data Love!
The conference spans EU and US time zones to make it friendlier for our worldwide community to participate. The event consists of two simultaneous session tracks streamed live with speaker Q&A and a highly engaging live hallway track. The lineup is amazing.
Let’s jump on a wonderful ride of the Data Love world and explore what our speakers have to say.
Our next wonderful speaker is Roksolana Diachuk, Big Data Developer at Captify.
Roksolana works as a Big Data Engineer at Captify. She is also a member of the Diversity&Inclusion taskforce and Futures Board there. She is passionate about Big Data, Scala, and Kubernetes and she always loves to learn something new. Roksolana is one of the Women Who Code Kyiv leads and mentors. Her hobbies include building technical topics around fairytales and discovering new cities.
Are your projects similar, do they have common focus points, or they can be completely different?
There are some similarities between the projects I worked on. Most of the time I had to work on building pipelines with data transformation and aggregation for further analysis and reporting by other teams. In some projects, there was more focus on the infrastructural side while in others it’s more around development only. A common thing for all of my projects is that I did not have to work with machine learning models and collaborate with data scientists.
Big data. Cloud data. ML, AI training data, and personally-identifying data. Data is all around us. The world is data-centric. What are some of the industries your clients come from?
I used to work with projects in the financial industry and now I work in the AdTech industry. Advertising crosses so many industries that it’s hard to name all of them. My current company has clients from the tech, financial, healthcare, automotive, streaming, social media, clothing, music, food industries. So the range of industries that require data insights is quite wide.
A lot of people are wondering about Data Engineers and Data Scientists, and the differences between them. What’s your favorite part about your role? What are you measured on? What do you expect when working in a tandem with Data Engineers/Scientists?
My favorite part is the possibility to work with challenging tasks that require not obvious solutions due to the nature of data (either it’s size or format). I love exploring the data and building transformations and aggregations on top of it.
I expect that both data engineers and data scientists can communicate their needs well and explain
What are the core skills that you think are important in your job, especially if you want to develop your Data Science/Engineering career?
Definitely, hard-core programming skills as a data engineer role is closest to the backend engineer role and it is required to write the code of high performance. It’s also important to have great analytical skills because data engineers, obviously, work with data a lot and get to explore this data and build all the solutions on top of it. In general, math skills, attention to detail, and great communication skills are important too same as for other roles in development.
‘I love working with data because…'
I love working with data because it is always challenging and because working with data helps to uncover the inner workings of multiple spheres in our daily life.
The industry demand for Data Engineers is constantly on the rise and with it more and more software engineers and recent graduates try to enter the field. Data Engineering is a discipline notorious for being framework-driven and it is often hard for newcomers to find the right ones to learn. What technology are you most excited about? Share top 3 data engineering frameworks to learn, please.
I think that the number one framework is Apache Spark. It is hard to imagine data engineers without Spark in their toolbelt. Apache Spark is constantly evolving with each new version bringing even more amazing features.
Another framework I’m really excited about is Delta Lake by Databricks which allows to track multiple versions of data and even travel back in time for those data versions. I’m sure that it’s going to change the data engineering landscape in the near future.
Also, a lot of data engineers get to work with machine learning models so MLOps frameworks have come into the spotlight recently. I find Kubeflow and Mlflow both worth attention.
We thank Roksolna for the thoughtful answers!
At the conference, she is going to speak on the topic: Alice in the world of machine learning.
If you want to attend Roksolana’s talk and to discuss some questions “in person” you can join us on the 16th of April!
The lineup of speakers is incredible. Topics are diverse. Suitable for any level. Interesting Q&A sessions in Spatial Chat. New career opportunities.
Data is all around you.
Data Love@_dataloveDo u know that after each speaker’s talk, you'll have extra time for Q&A sessions where you'll have an opportunity to get in touch with the speakers & the other attendees? What’s more, we're going to use a #SpatialChat platform - an absolutely new word in online communication!20:44 PM - 31 Mar 2021