Ultimately the data scientists need me more than I need them; I’m the reason their stuff is in production and runs smoothly.

I can't recommend the Data Engineer career enough for junior developers. It's how I started and what I pursued for 6 years (and I would love doing it again), and I feel like it gave me such an incredible foundation for future roles :- Actually big data (so, not something you could grep...) will trigger your code in every possible way. You quickly learn that with trillions of input, the probabily to reach a bug is either 0% or 100%. In turn, you quickly learn to write good tests.- You will learn distributed processing at a macro level, which in turn enlighten your thinking at a micro level. For example, even though the order of magnitudes are different, hitting data over network versus on disk is very much like hitting data on disk versus in cache. Except that when the difference ends up being in hours or days, you become much more sensible to that, so it's good training for your thoughts.- Data engineering is full of product decisions. What's often called data "cleaning" is in fact one of the import product decisions made in a company, and a data engineer will be consistently exposed to his company product, which I think makes for great personal development- Data engineering is fascinating. In adtech for example, logs of where ads are displayed are an unfiltered window on the rest of humanity, for the better or the worse. But it definitely expands your views on what the "average" person actually does on its computer (spoiler : it's mainly watching porn...), and challenges quite a bit what you might think is "normal"- You'll be plumbing technologies from all over the web, which might or might not be good news for you.So yeah, data engineering is great ! It's not harder than other specialties for developers, but imo, it's one of the fun ones !

When machine learning become hot 🔥 5-8 years ago, companies decided they need people that can make classifiers on data. But then frameworks like Tensorflow and PyTorch became really good, democratizing the ability to get started with deep learning and machine learning. This commoditized the data modelling skillset. Today, the bottleneck in helping companies get machine learning and modelling insights to production center on data problems.

There are 70% more open roles at companies in data engineering as compared to data science. As we train the next generation of data and machine learning practitioners, let’s place more emphasis on engineering skills.

TFX and Tensorflow run anywhere Python runs, and that’s a lot of places

since TFX and Tensorflow were built by Google, it has first-class support in the Google Cloud Platform.

After consideration, you decide to use Python as your programming language, Tensorflow for model building because you will be working with a large dataset that includes images, and Tensorflow Extended (TFX), an open-source tool released and used internally at Google, for building your pipelines.

These components has built-in support for ML modeling, training, serving, and even managing deployments to different targets.

Most data scientists feel that model deployment is a software engineering task and should be handled by software engineers because the required skills are more closely aligned with their day-to-day work. While this is somewhat true, data scientists who learn these skills will have an advantage, especially in lean organizations. Tools like TFX, Mlflow, Kubeflow can simplify the whole process of model deployment, and data scientists can (and should) quickly learn and use them. 

TFX Component called TensorFlow Model Analysis (TFMA) allows you to easily evaluate new models against current ones before deployment. 

In general, for smaller businesses like startups, it is usually cheaper and better to use managed cloud services for your projects. 

This has its pros and cons and may depend on your use case as well. Some of the pros to consider when considering using managed cloud services are:

The data is already in the cloud, so it may be better to build your ML system in the cloud. You’ll get better latency for I/O, easy scaling as data becomes larger (hundreds of gigabytes), and quick setup and configuration for any additional GPUs and TPUs. 

ML projects are never static. This is part of engineering and design that must be considered from the start. Here you should answer questions like:

The choice of framework is very important, as it can decide the continuity, maintenance, and use of a model. In this step, you must answer the following questions:

These questions are important as they will guide you on what frameworks or tools to use, how to approach your problem, and how to design your ML model.

DuckDB is an embeddable SQL OLAP database management system

An end-user (resource owner 👤) grants a printing service (app 📦) access to their photo (resource 🖼) hosted in a photo-sharing service (resource server 📚), without sharing their username and password. Instead, they authenticate directly with a server trusted by the photo-sharing service (authorization server 🛡), which issues the printing service delegation-specific credentials (access token 🔑).

from Docker Compose on a single machine, to Heroku and similar systems, to something like Snakemake for computational pipelines.

if what you care about is downtime, your first thought shouldn’t be “how do I reduce deployment downtime from 1 second to 1ms”, it should be “how can I ensure database schema changes don’t prevent rollback if I screw something up.”

The features Kubernetes provides for reliability (health checks, rolling deploys), can be implemented much more simply, or already built-in in many cases. For example, nginx can do health checks on worker processes, and you can use docker-autoheal or something similar to automatically restart those processes.

Scaling for many web applications is typically bottlenecked by the database, not the web workers.

Kubernetes might be useful if you need to scale a lot. But let’s consider some alternatives

Distributed applications are really hard to write correctly. Really. The more moving parts, the more these problems come in to play. Distributed applications are hard to debug. You need whole new categories of instrumentation and logging to getting understanding that isn’t quite as good as what you’d get from the logs of a monolithic application.

you need to spin up a complete K8s system just to test anything, via a VM or nested Docker containers.

“Kubernetes is a large system with significant operational complexity. The assessment team found configuration and deployment of Kubernetes to be non-trivial, with certain components having confusing default settings, missing operational controls, and implicitly defined security controls.”

Before you can run a single application, you need the following highly-simplified architecture

the Kubernetes codebase has significant room for improvement. The codebase is large and complex, with large sections of code containing minimal documentation and numerous dependencies, including systems external to Kubernetes.

Kubernetes has plenty of moving parts—concepts, subsystems, processes, machines, code—and that means plenty of problems.

Here is a high level comparison of the tools we reviewed above:

To address Hadoop’s complications and scaling challenges, Industry is now moving towards a disaggregated architecture, with Storage and Analytics layers very loosely coupled using REST APIs.

Hive is now trying to address consistency and usability. It facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage.

Delta Lake is an open-source platform that brings ACID transactions to Apache Spark™. Delta Lake is developed by Spark experts, Databricks. It runs on top of your existing storage platform (S3, HDFS, Azure) and is fully compatible with Apache Spark APIs.

Apache Iceberg is an open table format for huge analytic data sets. Iceberg adds tables to Presto and Spark that use a high-performance format that works just like a SQL table. Iceberg is focussed towards avoiding unpleasant surprises, helping evolve schema and avoid inadvertent data deletion.

in this disaggregated model, users can choose to use Spark for batch workloads for analytics, while Presto for SQL heavy workloads, with both Spark and Presto using the same backend storage platform.

rise of Hadoop as the defacto Big Data platform and its subsequent downfall. Initially, HDFS served as the storage layer, and Hive as the analytics layer. When pushed really hard, Hadoop was able to go up to few 100s of TBs, allowed SQL like querying on semi-structured data and was fast enough for its time.

These projects sit between the storage and analytical platforms and offer strong ACID guarantees to the end user while dealing with the object storage platforms in a native manner.

Disaggregated model means the storage system sees data as a collection of objects or files. But end users are not interested in the physical arrangement of data, they instead want to see a more logical view of their data.

ACID stands for Atomicity (an operation either succeeds completely or fails, it does not leave partial data), Consistency (once an application performs an operation the results of that operation are visible to it in every subsequent operation), Isolation (an incomplete operation by one user does not cause unexpected side effects for other users), and Durability (once an operation is complete it will be preserved even in the face of machine or system failure).

Currently this may be possible using version management of object store, but that as we saw earlier is at a lower layer of physical detail which may not be useful at higher, logical level.

Traditionally Data Warehouse tools were used to drive business intelligence from data. Industry then recognized that Data Warehouses limit the potential of intelligence by enforcing schema on write. It was clear that all the dimensions of data-set being collected could not be thought of at the time of data collection.

As explained above, users are no longer willing to consider inefficiencies of underlying platforms. For example, data lakes are now also expected to be ACID compliant, so that the end user doesn’t have the additional overhead of ensuring data related guarantees.

Commonly used Storage platforms are object storage platforms like AWS S3, Azure Blob Storage, GCS, Ceph, MinIO among others. While analytics platforms vary from simple Python & R based notebooks to Tensorflow to Spark, Presto to Splunk, Vertica and others.

Data Lakes that are optimized for unstructured and semi-structured data, can scale to PetaBytes easily and allowed better integration of a wide range of tools to help businesses get the most out of their data.

'Directed' means that the edges of the graph only move in one direction, where future edges are dependent on previous ones.

Several cryptocurrencies use DAGs rather than blockchain data structures in order to process and validate transactions.

graph data structure that uses topological ordering, meaning that the graph flows in only one direction, and it never goes in circles.

'Acyclic' means that it is impossible to start at one point of the graph and come back to it by following the edges.