Skills to Build for Data Engineering - Jun 4, 2020.
This article jumps into the latest skill set observations in the Data Engineering Job Market which could definitely add a boost to your existing career or assist you in starting off your Data Engineering journey.
Career Advice, Data Engineering, Skills
- Why and How to Use Dask with Big Data - Apr 15, 2020.
The Pandas library for Python is a game-changer for data preparation. But, when the data gets big, really big, then your computer needs more help to efficiency handle all that data. Learn more about how to use Dask and follow a demo to scale up your Pandas to work with Big Data.
Big Data, Dask, Data Engineering
- Five Interesting Data Engineering Projects - Mar 17, 2020.
As the role of the data engineer continues to grow in the field of data science, so are the many tools being developed to support wrangling all that data. Five of these tools are reviewed here (along with a few bonus tools) that you should pay attention to for your data pipeline work.
Dask, Data Engineering, dbt, DVC, Python
- Scaling the Wall Between Data Scientist and Data Engineer - Feb 17, 2020.
The educational and research focuses of machine learning tends to highlight the model building, training, testing, and optimization aspects of the data science process. To bring these models into use requires a suite of engineering feats and organization, a standard for which does not yet exist. Learn more about a framework for operating a collaborative data science and engineering team to deploy machine learning models to end-users.
Advice, Data Engineer, Data Engineering, Data Scientist, Deployment, DevOps, Machine Learning Engineer, MLflow, MLOps, Production
- Observability for Data Engineering - Feb 10, 2020.
Going beyond traditional monitoring techniques and goals, understanding if a system is working as intended requires a new concept in DevOps, called Observability. Learn more about this essential approach to bring more context to your system metrics.
Data Engineering, DevOps, Explainability, KPI, Monitoring, Time Series
7 Resources to Becoming a Data Engineer - Jan 7, 2020.
An estimated 8,650% growth of the volume of Data to 175 zetabytes from 2010 to 2025 has created an enormous need for Data Engineers to build an organization's big data platform to be fast, efficient and scalable.
Advice, Big Data, Cloud Computing, Data Engineering, Data Science, MOOC, SQL
- Four questions to help accurately scope analytics engineering project - Oct 9, 2019.
Being really good at scoping analytics projects is crucial for team productivity and profitability. You can consistently deliver on time if you work out the issue first, and these four questions can help you prepare.
Analytics, Data Engineering, dbt, Deployment
- The thin line between data science and data engineering - Sep 25, 2019.
Today, as companies have finally come to understand the value that data science can bring, more and more emphasis is being placed on the implementation of data science in production systems. And as these implementations have required models that can perform on larger and larger datasets in real-time, an awful lot of data science problems have become engineering problems.
Data Engineering, Data Science, Podcast
- Mongo DB Basics - Jun 5, 2019.
Mongo DB is a document oriented NO SQL database unlike HBASE which has a wide column store. The advantage of Document oriented over relation type is the columns can be changed as an when required for each case as opposed to the same column name for all the rows.
Big Data, Data Engineering, Data Science, MongoDB
- 7 “Gotchas” for Data Engineers New to Google BigQuery - Mar 28, 2019.
Here are some things that might take some getting used to when new to Google BigQuery, along with mitigation strategies where I’ve found them.
BigQuery, Data Engineer, Data Engineering, Google
- On Building Effective Data Science Teams - Mar 4, 2019.
We take a look at the qualities that make a successful data team in order to help business leaders and executives create better AI strategies.
CRISP-DM, Data Analyst, Data Engineering, Data Governance, Data Science Team, Machine Learning Engineer
- Things you should know when traveling via the Big Data Engineering hype-train - Oct 8, 2018.
Maybe you want to join the Big Data world? Or maybe you are already there and want to validate your knowledge? Or maybe you just want to know what Big Data Engineers do and what skills they use? If so, you may find the following article quite useful.
Big Data, Big Data Hype, Data Engineering, Hype
A Winning Game Plan For Building Your Data Science Team - Sep 18, 2018.
We need to understand the responsibilities, capabilities, expectations and competencies of the Data Engineer, Data Scientist and Business Stakeholder.
Data Engineering, Data Science, Data Science Team
- Scientific debt – what does it mean for Data Science? - May 23, 2018.
This article analyses scientific debt - what it is and what it means for data science.
Business, Data Engineering, Data Science, DataCamp, Technical Debt
- A Beginner’s Guide to Data Engineering – Part II - Mar 15, 2018.
In this post, I share more technical details on how to build good data pipelines and highlight ETL best practices. Primarily, I will use Python, Airflow, and SQL for our discussion.
Pages: 1 2
AirBnB, Data Engineering, Data Science, ETL, Pipeline, Python, SQL
37 Reasons why your Neural Network is not working - Aug 22, 2017.
Over the course of many debugging sessions, I’ve compiled my experience along with the best ideas around in this handy list. I hope they would be useful to you.
Pages: 1 2
Data Engineering, Data Preparation, Gradient Descent, Neural Networks
5 Career Paths in Big Data and Data Science, Explained - Feb 6, 2017.
Sexiest job... massive shortage... blah blah blah. Are you looking to get a real handle on the career paths available in "Data Science" and "Big Data?" Read this article for insight on where to look to sharpen the required entry-level skills.
Big Data, Career, Data Analyst, Data Engineering, Data Infrastructure, Data Science, Explained, Machine Learning
- Why the Data Scientist and Data Engineer Need to Understand Virtualization in the Cloud - Jan 25, 2017.
This article covers the value of understanding the virtualization constructs for the data scientist and data engineer as they deploy their analysis onto all kinds of cloud platforms. Virtualization is a key enabling layer of software for these data workers to be aware of and to achieve optimal results from.
Pages: 1 2
Cloud, Data Engineer, Data Engineering, Data Science, Data Scientist, Virtualization
