ZARZA We are Zarza, the prestigious firm behind major projects in information technology.
Sinopse do Episódio "Bytewax: The Next Data Processing Framework Generation in Python"
Spoiler Alert = Mind-Blowing ContentHave you ever asked yourself if someone has thought about how to unwrap and simplify stream processing?Or, would stream processing be available in Python using the best-of-breed libraries and Pythonic code and yet scale horizontally?If you're about to listen to this episode, this one may not only change your mind but also would drastically change the way you look at Python.Zander Matheson is a distinguished Python engineer and founder of the Bytewax company, which aims to simplify real-time stream processing using today's lingua franca, Python.Bytewax is an Open-Source Python framework that simplifies end-to-end data pipeline workflows. It offers a non-JVM approach to abstract and solves complex data problems.Hence, it's wicked fast and straightforward to use. It also offers a CLI where you can easily deploy the DataFlows.In this episode, we wade through the following topics:Python for Data Engineers and ScientistsPopularity and LibrariesBytewax & MotivatorsMain CapabilitiesAdoptionUpcoming FeaturesZander Matheson = https://www.linkedin.com/in/alexandermatheson/Bytewax = https://bytewax.io/ Luan Moreno = https://www.linkedin.com/in/luanmoreno/
Ouvir "Bytewax: The Next Data Processing Framework Generation in Python"
Mais episódios do podcast Engenharia de Dados [Cast]
- O Dia a Dia de um Analytics Engineer com o Time de Dados da Clicksign
- O Dia a Dia de um Arquiteto e Engenheiro de Dados com o Time de Dados da Clicksign
- Data Analytics com o Time de Dados da Clicksign
- Desafios na Construção de uma Plataforma de Dados no Kubernetes com o Time do Orion
- Gestão & Produtos de Dados com o Time de Dados da Clicksign
- Challenge in Bulding an Open-Source Community with Aaron Williams
- O Desafio da Engenharia de Dados no iFood com o Diretor de ML e Engenharia, Ivan Lima
- The Data Lakehouse Paradigm with Bill Inmon - The Father of Data Warehouse
- Querying Data with Trino from Earth to Space - Talk with Brian Olsen, a Developer Advocate at Starburst
- A Day in a Life of a Founding Engineer at StarTree: Apache Pinot with Neha Pawar
- Simplificando Projetos de Analytics utilizando dbt e Modern Data Stack com Matheus Willian, Head of Data Engineering na One Way Solution
- Conferência Kafka Summit 2023 London
- Cloudera CDP & Stream Processing para Real-Time Analytics com André Araújo, Field Engineer, Data in Motion na Cloudera
- Connecting Data Engineering and ML to Build an Efficient End-to-End Data Product Flow with Aurimas Griciūnas
- Dremio & Iceberg for Building an Open-Source Data Lakehouse with Dipankar Mazumdar, Data Advocate at Dremio
- Simplify Data Engineering Projects in Your Lakehouse with Delta Lake Framework with Matthew Powers & Denny Lee, Developer Advocates at Databricks
- Spark on Kubernetes [SPOK] with Hudson Buzby, Solutions Architect at Spot.io
- SQLMesh | Streamlining Python & SQL Transformations with Tobias Mao, Co-Founder & CTO at Tobiko Data
- O Poder do Lineage de Dados com Lucas Galindo & Gabs Ferreira da Alvin
- Kafka on Kubernetes using Strimzi with Jakub Scholz, Senior Principal Software Engineer at Red Hat
- A Day in a Life of Data Engineer at Netflix with Xinran Waibel
- Mastering Apache Airflow with Marc Lamberti, Head of Customer Education at Astronomer
- ETL no Airflow de Forma Inteligente e Escalável usando Astro Python SDK com Tatiana Martins, Staff Software Engineer na Astronomer
- Databricks como Plataforma de Lakehouse para Times de Dados
- Confluent Community Catalysts Brazukas: Dissecando o Apache Kafka [Round 1]
- A Day in a Life of a Co-Founder, Commiter & PMC Member of Apache Flink with Timo Walther
- Harnessing The Power of Data Observability on Elementary for dbt Users with Maayan Salom
- Enabling User-Facing Analytics using Apache Pinot with Kishore Gopalakrishna
- A Day in a Life of a Director of Airflow Engineering with Kaxil Naik at Astronomer
- Conferência Microsoft Ignite 2022: Anúncios e Novidades
- Cube: Reshaping Business Intelligence for Data Engineers with a Headless BI Platform
- Dremio: The De-Facto Open Data Lakehouse Platform with Alex Merced, Developer Advocate at Dremio
- Bytewax: The Next Data Processing Framework Generation in Python
- A Day in a Life of a Field CTO at Confluent - Data in Motion with Kai Waehner
- Sistema de OLAP em Tempo Real: ClickHouse para Big Data e Queries Ad-Hoc
- Conferência Snowflake Summit 2022: Anúncios e Novidades por Mateus Oliveira
- Conferência Data+AI Summit 2022 da Databricks: Anúncios e Novidades por Luan Moreno
- O Desafio da Engenharia de Dados com Filipe Comparini, Head de Dados da LuizaLabs
- Apache Cassandra: O Banco de Dados NoSQL de Missão Crítica e Tempo-Real da Fortune 500
- O Desafio da Engenharia de Dados com Wellington C. Faria, Lead Analytics Engineer do NuBank
- Apache Pulsar: A Plataforma de Streaming Distribuída mais Completa do Mercado com Samuel Matioli
- Cloudera CDP: Plataforma de Cloud Híbrida para Dados
- Casos de Uso e Experiências de Campo com Apache Spark
- Big Data no Google GCP com Lucas Magalhães
- Big Data na Amazon AWS com Carlos Barbosa
- Big Data no Microsoft Azure com Vitor Henrique Mendes
- Casos de Uso e Experiência de Campo com Apache Kafka
- Apache Kafka é um Banco de Dados Relacional?
- Big Data no Kubernetes
- Delta Lake: Storage Engine Escalável para Construção de um Data Lakehouse
- Data Warehouse vs. Data Lakehouse - Casos de Uso e Comparações com Orlando Marley
- Postgres como Plataforma de Dados Estruturados e Semi-Estruturados com Raul Oliveira
- The Brighter Side of COVID-19 for Data Engineers [English Tips]
- Strimzi - Adding Intelligence on Your Kafka on Kubernetes Deployment with Jakub Scholz
- The Rise of Apache Airflow for Data Pipeline Orchestration with Marc Lamberti
- Carreira de Data Engineer Internacional
- Engines de Processamento de Big Data - [Python e SQL]
- Processamento de Dados com Apache Spark - Casos de Uso, Problemas Comuns e Melhores Práticas
- Integração de Dados com Kafka Connect no Kubernetes [Strimzi Operator]
- Dw vs. Data Lake vs. MDW vs. Data Lakehouse para Pipeline de Dados
- Azure Flagship Product - Synapse Analytics as De-Facto to Build Data Pipelines and Reliable [MDWs]
- YugaByteDB - Banco de Dados Distribuído com Consistência e Transações [ACID]
- Arquiteturas de Big Data - Lambda, Kappa e Layered [Camadas]
- Apache Kafka no Kubernetes [Strimzi Operator]
In God we trust