Intro
The high demand for Data Engineers 🔢 👨🔧 👨🔧 may motivate software-savvy people to venture into the world of data. There is so much to learn, so little time. That is why concise summaries and code examples can be a useful mean to ease the strain on human's inherently limited memory.
Tip: look up unfamiliar words in the jargon.
1. Intro of the Intro
Data Scientist 👨🔧 and Data Scientist 👩🔬 are new job titles, where Data Analyst, BI Developer, and Software Engineer are well known titles.
Data Analyst: has the understanding of intermediate statistics, data mining tools, SQL, and data visualizations.
BI Developer: works closely with stakeholders to design and build reporting dashboards, integrating data from different sources, aggregating data with SQL, but not performing data analysis.
Data Scientists: turns raw data into pure insights using advanced statistics, math, and machine learning, cooperating with Data Engineers as some machine learning techniques require big and fast data.
Software Engineer: designs, writes, and aims to improve quality characteristics of software for computers or other electronic devices.
So what are companies looking for? They are looking for professionals! People with a desire to learn and master new stuff, and then become an authority in their field.
2. Love-Hate Relationship 👨🔧 💔 👩🔬
Data science and data engineering go hand in hand. A Data Engineer may aggregate batches of data overnight, so that the Data Scientist may train a machine learning model with it in a jupyter notebook, after which the 👨🔧 and 👩🔬 sit around the table to extract and refactor the useful bits of the code to forge a contract and automate the process of deploying the model. Let's specify this process:
Data Discovery: automatically discover data needed to train machine learning models.
Data Ingestion (ETL): the act of moving (and transforming) data - especially unstructured data - from where it is originated, into a system where it can be stored and analyzed such as Hadoop.
Data Exploration: visualizing the relationships of interest within the source data.
Data Preprocessing: encoding categorical variables, imputing missing variables, executing other corrections and augmentations.
Feature Engineering: deciding what features to create and check whether features work for a ML model.
Algorithm Selection: selecting the most optimal algorithm for a given feature set.
Training: training the ML model (may sometimes take a long time)
Evaluation: determine whether the model's accuracy, efficiency is sufficient.
Hyperparameter Tuning: choosing the set of optimal hyperparameters using methods such as:
Grid search
Random search
Bayesian optimization
Evolutionary optimization
Gradient-based optimization
Testing: writing tests to determine whether functionalities such as cleaning, encoding, test-train splitting, serializing, training, etc still work as intended.
Refactoring: transforming jupyter notebooks / scripts to a sound code project that can handle the data volume and velocity of the production environment.
Deployment:
create a REST API that exposes a model over http
dockerizing the project so that it can scale and run in isolation
applying continuous integration to automate collaboration
apply continuous deployment so that new versions of the project are tested, built and deployed automatically
exposing the model to the systems that need it
Provisioning: using Kubernetes or Docker Swarm to automatically scale up or down the service.
It is understood that 👨🔧 are often critical of the code quality and scalability of the piece of code delivered by 👩🔬, which in return are unhappily surprised when they have to adjust their code only because the 👨🔧 is overly critical, all the while the data provided by the 👨🔧 wasn't all that well prepared. But despite the hate, there exists love when they work as a team, and get that model deployed successfully!
3. Big Data
Where Data Analysts and BI Developers dealt with Data Warehouses with static structured Terabytes of data, Data Engineers deal with:
Volume: petabytes of data
Variety: structured and unstructured data of any type and format
Velocity: flowing streams of fast data
Organizations grow into Big Data as they become experienced with processing data into valuable information, from left to right:
From here on, this gitbook will focus on two sets of skills:
Architecture, Consulting, Project Management, Collaboration, Mature Decisions
Technologies, Services
The first set of skills is something that professionals should strive to master. The second set contains summaries of technologies that will continuously change over time.
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