data-engineering-nd
Apache Spark
data-engineering-nd | Apache Spark | |
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7 | 101 | |
8 | 38,414 | |
- | 0.6% | |
0.0 | 10.0 | |
about 2 years ago | about 10 hours ago | |
Jupyter Notebook | Scala | |
- | Apache License 2.0 |
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data-engineering-nd
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Data Pipelines explained with Airflow
In the following lines I am doing a write-up about everything I learned about data pipelines at the Udacity online class. It gives a general overview about data pipelines and provides also the core concepts of Airflow and some links to code examples on github.
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Run Spark locally with Docker
You can find the code also here.
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Spark for beginners - and you
Coding examples here.
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Cloud computing quickstart
IaC - Infrastructure as Code Example with boto3
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Relational data models
If you need a higher resolution please use this page
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Get started with data engineering
In addition you can find the according exercises on my github account.
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Structured Query Language
If the resolution here is too low - in case you really want to read it - you can find a higher resolution here.
Apache Spark
- "xAI will open source Grok"
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Groovy 🎷 Cheat Sheet - 01 Say "Hello" from Groovy
Recently I had to revisit the "JVM languages universe" again. Yes, language(s), plural! Java isn't the only language that uses the JVM. I previously used Scala, which is a JVM language, to use Apache Spark for Data Engineering workloads, but this is for another post 😉.
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🦿🛴Smarcity garbage reporting automation w/ ollama
Consume data into third party software (then let Open Search or Apache Spark or Apache Pinot) for analysis/datascience, GIS systems (so you can put reports on a map) or any ticket management system
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Go concurrency simplified. Part 4: Post office as a data pipeline
also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc.
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Five Apache projects you probably didn't know about
Apache SeaTunnel is a data integration platform that offers the three pillars of data pipelines: sources, transforms, and sinks. It offers an abstract API over three possible engines: the Zeta engine from SeaTunnel or a wrapper around Apache Spark or Apache Flink. Be careful, as each engine comes with its own set of features.
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Apache Spark VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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Integrate Pyspark Structured Streaming with confluent-kafka
Apache Spark - https://spark.apache.org/
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Spark – A micro framework for creating web applications in Kotlin and Java
A JVM based framework named "Spark", when https://spark.apache.org exists?
- Rest in Peas: The Unrecognized Death of Speech Recognition (2010)
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PySpark SparkSession Builder with Kubernetes Master
I recently saw a pull request that was merged to the Apache/Spark repository that apparently adds initial Python bindings for PySpark on K8s. I posted a comment to the PR asking a question about how to use spark-on-k8s in a Python Jupyter notebook, and was told to ask my question here.
What are some alternatives?
udacimak - Udacity Nanodegree and Course Downloader
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
quilt - Quilt is a data mesh for connecting people with actionable data
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
Data-Engineering-Projects - Personal Data Engineering Projects
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
migrate - Database migrations. CLI and Golang library.
Scalding - A Scala API for Cascading
Apache Hadoop - Apache Hadoop
mrjob - Run MapReduce jobs on Hadoop or Amazon Web Services
luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing