Data Engineering Weekly

NOVEMBER 11, 2024

The blog outlines two main approaches for building these models: the Unified Embedding Decoder Architecture and the Cross-Modality Attention Architecture. The blog highlights the importance of iterating on this process, continuously refining the LLM judge by learning from the expert's insights, and ensuring alignment with business goals.

Data Engineer 57

Data Engineer Data Engineering Engineering Datasets 57

Pinterest Engineering

OCTOBER 11, 2024

In Part 2 of our blog series, we described how we were able to integrate Ray(™) into our existing ML infrastructure. In this blog post, we will discuss a second type of popular application of Ray(™) at Pinterest: offline batch inference of ML models. Dataset execution is pipelined so that multiple execution stages can run in parallel.

Datasets 63

Datasets Software Engineering Software Engineer Metadata 63

DataKitchen

DECEMBER 9, 2022

DataOps involves collaboration between data engineers, data scientists, and IT operations teams to create a more efficient and effective data pipeline, from the collection of raw data to the delivery of insights and results. The data scientists and IT professionals were amazed, and they couldn’t believe their eyes.

Machine Learning 52

Machine Learning Data Preparation Government Data Analytics 52

Christophe Blefari

APRIL 19, 2024

It was trained on a large dataset containing 15T tokens (compared to 2T for Llama 2). This is super interesting because it details important steps of the generative process. This blog shows how you can use Gen AI to evaluate inputs like translations with added reasons. — A great blog to answer a great question.

MySQL 130

MySQL Data Datasets SQL 130

Maxime Beauchemin

JANUARY 7, 2018

Batch data processing  — historically known as ETL —  is extremely challenging. In this post, we’ll explore how applying the functional programming paradigm to data engineering can bring a lot of clarity to the process. Late arriving facts Late arriving facts can be problematic with a strict immutable data policy.

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Data Engineer Data Engineering Data Process Process 183

ProjectPro

JANUARY 25, 2022

Here’s What You Need to Know About PySpark This blog will take you through the basics of PySpark, the PySpark architecture, and a few popular PySpark libraries , among other things. Finally, you'll find a list of PySpark projects to help you gain hands-on experience and land an ideal job in Data Science or Big Data.

Big Data 52

Big Data Data Process Process Kafka 52

Cloudyard

OCTOBER 1, 2024

Read Time: 3 Minute, 57 Second Pandas, a popular Python library, is a fantastic tool for small to moderately sized data, but it struggles with large-scale datasets. This blog will explore the key differences between Pandas DataFrames and Snowpark DataFrames (enhanced by Modin ), demonstrate their respective strengths.

Datasets 52

Datasets Big Data Data Warehouse Cloud 52

Data Engineering Weekly

JULY 14, 2024

[link] Discord: How Discord Uses Open-Source Tools for Scalable Data Orchestration & Transformation Discord writes about its migration journey from a homegrown orchestration engine to Dagster. Streaming execution to process a small chunk of data at a time. Intermediate spilling to disk while computing aggregations.

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Data Engineer Data Engineering Engineering Unstructured Data 98

Data Engineering Weekly

SEPTEMBER 18, 2024

The Rise of Data Observability Data observability has become increasingly critical as companies seek greater visibility into their data processes. This growing demand has found a natural synergy with the rise of the data lake. Visualization tools that map out pipeline dependencies are particularly helpful here.

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Data Lake Data Pipeline Unstructured Data Datasets 98

phData: Data Engineering

NOVEMBER 8, 2024

The world we live in today presents larger datasets, more complex data, and diverse needs, all of which call for efficient, scalable data systems. Open Table Format (OTF) architecture now provides a solution for efficient data storage, management, and processing while ensuring compatibility across different platforms.

Architecture 52

Architecture Systems Data Lake Google Cloud 52

Data Engineering Weekly

SEPTEMBER 29, 2024

[link] Grab: Enabling conversational data discovery with LLMs at Grab. Grab confirms my observation that users abandoned their searches in 18% of sessions without clicking on any dataset. The article details how bypassing intermediate storage steps reduces latency and improves data processing speed.

Data Engineer 62

Data Engineer Data Engineering Engineering SQL 62

Databand.ai

AUGUST 30, 2023

Validity: Adherence to predefined formats, rules, or standards for each attribute within a dataset. Uniqueness: Ensuring that no duplicate records exist within a dataset. Integrity: Maintaining referential relationships between datasets without any broken links.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

Data Science Blog: Data Engineering

AUGUST 22, 2024

Tools like BiG EVAL are leading data quality field for all technical systems in which data is transported and transformed. Real-Time Data Processing Businesses are adopting technologies that can process and analyze data instantly due to the need for real-time insights.

Data Preparation 52

Data Preparation Transportation High Quality Data Data Science 52

Ascend.io

NOVEMBER 14, 2024

Why Future-Proofing Your Data Pipelines Matters Data has become the backbone of decision-making in businesses across the globe. The ability to harness and analyze data effectively can make or break a company’s competitive edge. In this blog post, we’ll explore key strategies for future-proofing your data pipelines.

Data Pipeline 52

Data Pipeline Amazon Web Services Data Integration Data 52

Ascend.io

OCTOBER 28, 2024

Data transformation helps make sense of the chaos, acting as the bridge between unprocessed data and actionable intelligence. You might even think of effective data transformation like a powerful magnet that draws the needle from the stack, leaving the hay behind.

Raw Data 52

Raw Data Datasets Aggregated Data Data Pipeline 52

Pinterest Engineering

NOVEMBER 18, 2024

Liang Mou; Staff Software Engineer, Logging Platform | Elizabeth (Vi) Nguyen; Software Engineer I, Logging Platform | In today’s data-driven world, businesses need to process and analyze data in real-time to make informed decisions. What is Change Data Capture? Why is CDC Important? or its affiliates.

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Kafka MySQL Database Software Engineering 46

Pinterest Engineering

SEPTEMBER 26, 2023

We have published a detailed blog post of its modeling architecture. At the end of this pipeline, the data with training features are ingested in the database. Figure 1: hybrid logging for features On a daily basis, the features are joined with the labels to produce the final training dataset.

Software Engineering 103

Software Engineering Software Engineer Machine Learning Datasets 103

Workfall

JULY 18, 2023

Behind the scenes, a team of data wizards tirelessly crunches mountains of data to make those recommendations sparkle. As one of those wizards, we’ve seen the challenges we face: the struggle to transform massive datasets into meaningful insights, all while keeping queries fast and our system scalable.

Datasets 77

Datasets Entertainment Data Workflow Data 77

Netflix Tech

NOVEMBER 20, 2023

by Jun He , Yingyi Zhang , and Pawan Dixit Incremental processing is an approach to process new or changed data in workflows. The key advantage is that it only incrementally processes data that are newly added or updated to a dataset, instead of re-processing the complete dataset.

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Process Data Pipeline Datasets SQL 86

DataKitchen

NOVEMBER 5, 2024

The Race For Data Quality In A Medallion Architecture The Medallion architecture pattern is gaining traction among data teams. It is a layered approach to managing and transforming data. By systematically moving data through these layers, the Medallion architecture enhances the data structure in a data lakehouse environment.

Architecture 79

Architecture Raw Data Pipeline-centric Data Ingestion 79

Data Engineering Weekly

NOVEMBER 20, 2022

Google AI: The Data Cards Playbook: A Toolkit for Transparency in Dataset Documentation Google published Data Cards , a dataset documentation framework aimed at increasing transparency across dataset lifecycles. link] The short YouTube video gives a nice overview of the Data Cards.

Data Engineer 52

Data Engineer Data Engineering Engineering Datasets 52

Cloudera

JULY 19, 2023

As Ozone scales to exabytes of data, it is important to ensure that Ozone Manager can perform at scale. In this blog post, we will highlight the work done recently to improve the performance of Ozone Manager to scale to exabytes of data. The hardware specifications are included at the end of this blog.

Management 70

Management Metadata Datasets Architecture 70

Knowledge Hut

DECEMBER 28, 2023

Hadoop is a popular open-source framework that stores and processes large datasets in a distributed manner. Organizations are increasingly interested in Hadoop to gain insights and a competitive advantage from their massive datasets. Hadoop can store data and run applications on cost-effective hardware clusters.

Hadoop 52

Hadoop Project Datasets Big Data 52

DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Mastering Data Production (#3) Introduction Managing the production phase of data analytics is a daunting challenge. Overseeing multi-tool, multi-dataset, and multi-hop data processes ensures high-quality outputs. Have I Checked The Raw Data And The Integrated Data?

Raw Data 50

Raw Data Data Ingestion Datasets Data 50

Knowledge Hut

APRIL 25, 2024

It is especially true in the world of big data. If you want to stay ahead of the curve, you need to be aware of the top big data technologies that will be popular in 2024. In this blog post, we will discuss such technologies. Big data is a term that refers to the massive volume of data that organizations generate every day.

Big Data 98

Big Data Technology Hadoop NoSQL 98

RandomTrees

AUGUST 21, 2024

Python’s integration with Power BI offers a range of benefits: Enhanced Data Analysis : Python’s extensive libraries such as Pandas, NumPy, and SciPy enable advanced data processing and statistical analysis that may be beyond Power BI’s built-in capabilities. Why Integrate Python with Power BI?

BI 52

BI Python Datasets Machine Learning 52

Engineering at Meta

MAY 22, 2024

To efficiently process data generated by billions of people, Data Infrastructure teams at Meta have built a variety of data management systems over the last decade, each targeted to a somewhat specific data processing task. As much as possible, our focus was to use open standards in these APIs.

Data Management 91

Data Management Management Data SQL 91

Cloudera

FEBRUARY 8, 2021

This is part 2 in this blog series. You can read part 1, here: Digital Transformation is a Data Journey From Edge to Insight. The first blog introduced a mock connected vehicle manufacturing company, The Electric Car Company (ECC), to illustrate the manufacturing data path through the data lifecycle.

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Data Pipeline Building Manufacturing Data Warehouse 118

Pinterest Engineering

JULY 25, 2023

Soam Acharya | Data Engineering Oversight; Keith Regier | Data Privacy Engineering Manager Background Businesses collect many different types of data. Each dataset needs to be securely stored with minimal access granted to ensure they are used appropriately and can easily be located and disposed of when necessary.

Big Data 98

Big Data Accessibility Accessible Hadoop 98

Ascend.io

SEPTEMBER 6, 2024

AI offers innovative solutions to enhance data integrity, addressing long standing challenges faced by engineers. By automating data cleansing processes and providing predictive analytics, AI not only improves data accuracy but also ensures consistency and reliability.

Data Cleanse 52

Data Cleanse Data Workflow Data Pipeline Machine Learning 52

LinkedIn Engineering

MARCH 23, 2023

Co-Authors: Yuhong Cheng , Shangjin Zhang , Xinyu Liu, and Yi Pan Efficient data processing is crucial in reducing learning curves, simplifying maintenance efforts, and decreasing operational complexity. In this blog post, we will share our progress, challenges, and lessons learned from implementing Apache Beam.

Process 97

Process Lambda Architecture Kafka Datasets 97

LinkedIn Engineering

APRIL 4, 2023

This blog post will provide an overview of how we approached metrics selection and design, system architecture and key product features. System Architecture Overview Setup We wanted to build a single data processing pipeline that would be efficient and scalable as more metrics are added. from the metric’s processing logic (i.e.

MySQL 122

MySQL Datasets Software Engineering Software Engineer 122

Databand.ai

JULY 17, 2023

Aim to automate processes: Automation is a key aspect of both DataOps and MLOps as it helps streamline workflows, reduce errors, increase efficiency, and ensure consistency across projects. However, if machine learning models are at the core of your business operations, MLOps will provide better support.

Data Pipeline 52

Data Pipeline Machine Learning High Quality Data BI 52

Cloudyard

APRIL 4, 2024

However, the volume of daily transaction data poses challenges in effectively segmenting customers and optimizing engagement. This blog post explores how Snowpark, a powerful tool for data processing within Snowflake, can be used to perform RFM segmentation and unlock actionable customer insights.

Retail 40

Retail Data Ingestion Metadata Database 40

Cloudera

JUNE 26, 2023

It can provide a complete solution for data exploration, data analysis, data visualization, viz applications, and model deployment at scale. Impala works best for analytical performance with properly designed datasets (well-partitioned, compacted). Visit our Data and IT Leaders page to learn more.

ETL Tools 75

ETL Tools Programming Language Datasets Data Pipeline 75

Databand.ai

JULY 5, 2023

Improved Collaboration Among Teams Data engineering teams frequently collaborate with other departments, such as analysts or scientists, who depend on accurate datasets for their tasks. Boosting Operational Efficiency A well-monitored data pipeline can significantly increase an organization’s operational efficiency.

Data Pipeline 52

Data Pipeline Data Lake Data Warehouse Datasets 52

Rockset

MARCH 18, 2024

That’s because successfully deploying an AI application requires retrieval augmented generation or “RAG” pipelines, processing real-time data streams, chunking data, generating embeddings, storing embeddings and running vector search. What is RAG? Why use Confluent Cloud for Apache Flink and Rockset for RAG?

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Cloud Building Metadata Kafka 64