Data Engineering Podcast

JUNE 15, 2020

Summary Machine learning is a process driven by iteration and experimentation which requires fast and easy access to relevant features of the data being processed. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. Email hosts@dataengineeringpodcast.com ) with your story.

Machine Learning 100

Machine Learning Google Cloud Kafka Data Engineering 100

Netflix Tech

MARCH 7, 2024

Data: Fast Data Our main data lake is hosted on S3, organized as Apache Iceberg tables. For ETL and other heavy lifting of data, we mainly rely on Apache Spark. In addition to Spark, we want to support last-mile data processing in Python, addressing use cases such as feature transformations, batch inference, and training.

Systems 90

Systems Media Machine Learning Data Warehouse 90

Data Engineering Podcast

MAY 4, 2020

How do you handle versioning of pipelines and validation of new iterations prior to production release? Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. How do you handle versioning of pipelines and validation of new iterations prior to production release?

Data Pipeline 100

Data Pipeline Hadoop Big Data Data 100

Data Engineering Weekly

MARCH 26, 2023

.” [link] Rittman Analytics: ChatGPT, Large Language Models and the Future of dbt and Analytics Consulting Very fascinating to read about the potential impact of LLM in the future of dbt and analytical consulting. The author predicts we are at the beginning of the industrial revolution of computing.

Data Engineering 52

Data Engineering Data Engineer Engineering Lambda Architecture 52

Data Engineering Podcast

JANUARY 26, 2020

Unfortunately they are also complex and difficult to test, leading to a significant amount of technical debt which contributes to slower iteration cycles. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. Email hosts@dataengineeringpodcast.com ) with your story.

Data Pipeline 100

Data Pipeline PostgreSQL Media Data Validation 100

Knowledge Hut

MAY 2, 2024

Here come the frameworks like Apache Spark and MapReduce to our rescue and help us to get deep insights into this huge amount of structured, unstructured, and semi-structured data and make more sense of it. Since its launch Spark has seen rapid adoption and growth. The demand for Spark is increasing at a very fast pace.

Scala 94

Scala Hadoop Datasets Java 94

Grouparoo

OCTOBER 6, 2021

Last month, we decided that we should all read a book and talk about it as a company. This was the first book I have read in this series and I liked the format. I read the old-fashioned hard copy, but I was told by people using the Kindle version that the author pictures were of random size. Be adaptable. What does that do?

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Zalando Engineering

JULY 12, 2017

In this blog post we describe the generation of such events using Apache Flink, and share our experiences and lessons learned in the process. You can read more on why we have chosen Apache Flink over other stream processing frameworks here: Apache Showdown: Flink vs. Spark.

Process 40

Process Kafka AWS Architecture 40

Knowledge Hut

JANUARY 18, 2024

Apache Spark Apache Spark is a batch-processing framework with the capability of stream processing and making it a hybrid framework. Spark is most notably easy to use, and it’s easy to write applications in Java, Scala, Python, and R. Spark can be run on a single machine, with one executor for every CPU core.

Data Process 52

Data Process Process Hadoop Scala 52

AltexSoft

JULY 18, 2023

To some, the word Apache may bring images of Native American tribes celebrated for their tenacity and adaptability. On the other hand, the term spark often brings to mind a tiny particle that, despite its size, can start a large fire. What is Apache Spark? Apache Spark components.

Big Data 52

Big Data Data Process Process Hadoop 52

Towards Data Science

FEBRUARY 19, 2024

Image from Unsplash Building a Semantic Book Search: Scale an Embedding Pipeline with Apache Spark and AWS EMR Serverless Using OpenAI’s Clip model to support natural language search on a collection of 70k book covers In a previous post I did a little PoC to see if I could use OpenAI’s Clip model to build a semantic book search.

AWS 61

AWS Building Bytes Python 61

Cloudera

OCTOBER 14, 2020

Why choose K8s for Apache Spark. Apache Spark unifies batch processing, real-time processing, stream analytics, machine learning, and interactive query in one-platform. Scheduling challenges to run Apache Spark on K8s. For instance, Spark driver pods need to be scheduled earlier than worker pods.

Big Data 117

Big Data Machine Learning Cloud Management 117

Netflix Tech

DECEMBER 3, 2022

job: id: ddl type: Spark spark: script: $S3{./ddl/dataflow_sparksql_sample.sql} See example below: - template: id: wap type: wap tables: - ${CATALOG}/${DATABASE}/${TABLE} write_jobs: - job: id: write type: Spark spark: script: $S3{./src/sparksql_write.sql} test_sparksql_write.py test_sparksql_write.py

Data Pipeline 143

Data Pipeline Scala Metadata Food 143

Towards Data Science

DECEMBER 24, 2023

My learnings from Databricks customer engagements Figure 1: a technical diagram of how to write apache spark. After working with ~15 of the largest retail organizations for the past 18 months, here are the Spark tips I commonly repeat. 0 — Quick Review Quickly, let’s review what spark does… Spark is a big data processing engine.

Scala 83

Scala SQL Java Python 83

Data Engineering Podcast

JUNE 16, 2019

Delta Lake is an open source, opinionated framework built on top of Spark for interacting with and maintaining data lake platforms that incorporates the lessons learned at DataBricks from countless customer use cases. By keeping a copy of all iterations of a data set there is the opportunity for a great deal of additional cost.

Data Lake 100

Data Lake Lambda Architecture Data Warehouse Hadoop 100

Data Engineering Podcast

MAY 18, 2021

What are some of the ideas or assumptions that you had at the beginning of this project that have had to be revisited as you iterate on the definition and implementation? Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. __init__ Episode Apache Spark EXIF JSON Schema OpenTelemetry Podcast.__init__

Metadata 100

Metadata Kafka Data Warehouse Hadoop 100

Cloudera

OCTOBER 17, 2023

Five Takeaways from Current 2023: 1- The people have spoken and Apache Flink is the de facto standard for stream processing This may seem obvious to many who are already familiar with Flink, but it is worth pointing out. It makes perfect sense that Apache Flink has emerged as the standard. Expecting a product to be GA’d.

Database-centric 79

Database-centric Kafka Pipeline-centric Database 79

ProjectPro

NOVEMBER 22, 2021

PySpark runs a completely compatible Python instance on the Spark driver (where the task was launched) while maintaining access to the Scala-based Spark cluster access. This enables them to integrate Spark's performant parallel computing with normal Python unit testing. Is PySpark the same as Spark? appName('ProjectPro').getOrCreate()

Hadoop 52

Hadoop Python Datasets Metadata 52

Data Engineering Weekly

MAY 21, 2023

However, as with any initial system iteration, the dbt model contract implementation has pros and cons. link] Instacart: How Instacart Ads Modularized Data Pipelines With Lakehouse Architecture and Spark Instacart writes about its journey of building its ads measurement platform. I’m sure it will evolve as the adoption increases.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Pipeline 52

ProjectPro

NOVEMBER 11, 2014

Confused over which framework to choose for big data processing - Hadoop MapReduce vs. Apache Spark. Hadoop and Spark are popular apache projects in the big data ecosystem. Apache Spark is an improvement on the original Hadoop MapReduce component of the Hadoop big data ecosystem.

Hadoop 40

Hadoop Scala Machine Learning Java 40

Scribd Technology

MAY 18, 2021

Streaming data from Apache Kafka into Delta Lake is an integral part of Scribd’s data platform, but has been challenging to manage and scale. We use Spark Structured Streaming jobs to read data from Kafka topics and write that data into Delta Lake tables. Our first Spark-based attempt at solving this problem falls under “both.”

Kafka 52

Kafka Data Warehouse Bytes Metadata 52

ProjectPro

JANUARY 31, 2023

HBase storage is ideal for random read/write operations, whereas HDFS is designed for sequential processes. Typically, data processing is done using frameworks such as Hadoop, Spark, MapReduce, Flink, and Pig, to mention a few. Commodity hardware is the fundamental hardware resource required to operate the Apache Hadoop framework.

Big Data 52

Big Data Hadoop AWS Relational Database 52

U-Next

OCTOBER 25, 2022

In data manipulation, data is organized in a way that makes it easier to read, or that makes it more visually appealing, or that makes it more structured. The use of data manipulation tools enables you to simplify the reading and organizing of data. Apache spark: Using Apache Spark, you can manipulate data quickly.

Business Intelligence 40

Business Intelligence Raw Data Data Cleanse Data Analysis 40

Cloudera

DECEMBER 20, 2023

Each iteration requires more compute and the limitation imposed by Moore’s Law quickly moves that task from single compute instances to distributed compute. Ray has emerged as a popular framework because of its superior performance over Apache Spark for distributed AI compute workloads.

Amazon Web Services 73

Amazon Web Services AWS Machine Learning Datasets 73

Cloudera

NOVEMBER 13, 2020

Apache Impala is synonymous with high-performance processing of extremely large datasets, but what if our data isn’t huge? It turns out that Apache Impala scales down with data just as well as it scales up. About 31% of the queries, spread out across the first three buckets, read from 0 to 100 rows. Execution Engine.

Metadata 144

Metadata Coding SQL Database 144

Cloudera

MAY 24, 2021

For example, CDH users would convert their Apache Sentry implementation to Apache Ranger using an automated conversion tool and HDP users would transition Ambari configurations to Cloudera Manager using AM2CM. But, Spark 1.6 to Spark 2.X The in-place upgrade requires cluster downtime. X code updates. . Side-car Migration.

Cloud 82

Cloud Metadata Utilities Process 82

ProjectPro

SEPTEMBER 16, 2021

Metadata Store : Metadata for more significant and evolving datasets can be housed in metadata stores Model Registry : Logging models are done in the model registry; this setup helps reflect on multiple iterations. It is used to build ETL pipelines for Feature Stores using Apache Spark.

Project 52

Project Amazon Web Services Machine Learning Data Science 52

Zalando Engineering

NOVEMBER 22, 2017

Using Apache and the Kafka Streams API with Scala on AWS for real-time fashion insights This piece was originally published on confluent.io For our purposes, we just pick a set number of iterations, execute them, and then accept the results from that point. Technically, you don’t have to iterate.

Kafka 40

Kafka Scala Hadoop Algorithm 40

DataKitchen

APRIL 13, 2021

Read the complete blog below for a more detailed description of the vendors and their capabilities. Apache Oozie — An open-source workflow scheduler system to manage Apache Hadoop jobs. They make it easy to deploy and manage your own Apache Airflow webserver, so you can get straight to writing workflows.

Consulting 123

Consulting Machine Learning Data Science Data Pipeline 123

Cloudera

OCTOBER 17, 2017

Read Deep Learning: A Guide for Enterprise Architects , available here. Data scientists working with CDSW can use any deep learning framework that has a Python, R, or Scala API, including TensorFlow, Keras, Theano, Microsoft Cognitive Toolkit (CNTK) , Caffe, PyTorch , DL4J, Apache MXNet , Torch, and BigDL. Apache Spark in Cloudera.

Deep Learning 48

Deep Learning Scala Medical Data Science 48

Pinterest Engineering

MAY 2, 2023

At Pinterest, most of our streaming jobs are built on top of Apache Flink , because Flink is a mature streaming framework with a lot of adoption in the industry. However, when we process a new event for a user, we do not want to read the existing N events, update them, and then write them all back to the respective dataset.

Lambda Architecture 65

Lambda Architecture Datasets Software Engineer Software Engineering 65

Zalando Engineering

NOVEMBER 3, 2016

In this vein, we have iterated the way we do data science, specially early stage exploratory data science with large datasets. Libraries such as scikit-learn allow us to iterate really fast around ideas. Using EMR clusters, reading S3 files, or having immutable experiments is not straightforward with this approach.

Data Science 52

Data Science Cloud Scala Datasets 52

Airbnb Tech

NOVEMBER 3, 2020

We’ve assembled top-notch data science and engineering teams, built industry-leading data infrastructure, and launched numerous successful open source projects, including Apache Airflow and Apache Superset. Meanwhile, Spark had reached maturity and the company had a growing expertise in this domain. This is discussed below.

Data Warehouse 52

Data Warehouse Scala Datasets Data Engineering 52

Afterpay Tech

SEPTEMBER 24, 2020

User friendly learning curve : We prefer languages such as Python and SQL our users are already familiar with, so that they can focus on delivering business value, rather than forcing them to learn more engineering-focused technologies like Apache Spark, needed to get most out of certain commercial platforms. What did we learn?

Data Science 52

Data Science Building Data Lake Machine Learning 52

Cloudera

DECEMBER 12, 2018

Let’s talk about big data and Apache Impala. So many of our readers might not be familiar with Apache Impala. Can you provide some context on how Apache Impala came about? But many of the execution tricks that SQL engines use, like Teradata, are being implemented in Apache Impala. Interesting times. Greg Rahn: Sure.

Data Warehouse 42

Data Warehouse Relational Database Hadoop BI 42

phData: Data Engineering

JANUARY 3, 2022

You can read the full guide without giving us your email — keep scrolling !) You also need to be concerned with things like availability, durability, consistency, cost, and how to iterate on a data product. Now part of the Apache Foundation, it originally was developed by CollabNet, Inc. Want to Save This eBook for Later?

IT 52

IT AWS Software Engineer Software Engineering 52

ProjectPro

JULY 27, 2021

Read more for a detailed comparison between data scientists and data engineers. Apache Hadoop is a collection of open-source libraries for processing large amounts of data. By using the *, a variable associated with it becomes iterable. How is a data architect different from a data engineer?

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40