Palantir

APRIL 10, 2023

Our customers who rely on legacy code and infrastructure frequently face the following challenges: Modernizing is disruptive: Too often, the only options presented for modernization are highly disruptive — data lakes, code base overhauls, and multi-year roadmaps. Fuse disparate data to quickly produce new analyses.

Systems 52

Systems Engineering Data Lake Coding 52

DoorDash Engineering

NOVEMBER 21, 2022

While building out DashMart’s internal inventory management system to help DashMart associates manage inventory, the DashMart engineering team came to realize that since the inventory tables were so core and foundational to different operational use cases in a DashMart, some actions or code must be triggered every time the inventory level changes.

Data Process 57

Data Process Process Kafka Database 57

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.

Data Engineering 183

Data Engineering Data Engineer 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

Towards Data Science

APRIL 30, 2024

Balancing correctness, latency, and cost in unbounded data processing Image created by the author. Intro Google Dataflow is a fully managed data processing service that provides serverless unified stream and batch data processing. Apache Beam lets users define processing logic based on the Dataflow model.

Google Cloud 61

Google Cloud Process Cloud Lambda Architecture 61

Cloudera

MAY 28, 2024

Modern Data Architectures are Ready for the Future There is an important distinction between data architecture and modern data architecture. When we talk about modern data architecture, there are several unique benefits to this kind of approach.

Data Architecture 75

Data Architecture Architecture Data Lake Data Warehouse 75

Engineering at Meta

MAY 22, 2024

In recent years, Meta’s data management systems have evolved into a composable architecture that creates interoperability, promotes reusability, and improves engineering efficiency. Data is at the core of every product and service at Meta. We needed to change our thinking to be able to move faster.

Data Management 86

Data Management Management Data SQL 86

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. Look for a suitable big data technologies company online to launch your career in the field.

Big Data 98

Big Data Technology NoSQL Hadoop 98

LinkedIn Engineering

OCTOBER 19, 2023

Authors: Bingfeng Xia and Xinyu Liu Background At LinkedIn, Apache Beam plays a pivotal role in stream processing infrastructures that process over 4 trillion events daily through more than 3,000 pipelines across multiple production data centers.

Process 119

Process Lambda Architecture Kafka Machine Learning 119

Data Engineering Weekly

DECEMBER 10, 2023

link] Evidently: ML system design - 300 case studies to learn from An amazing compilation of ML system design articles from various companies. The blog is an excellent comparison study of Ray vs. Dask’s performance. From a data architecture point of view, this enables a lot of flexibility in integrating multiple systems.

Data Engineering 76

Data Engineering Data Engineer Engineering Metadata 76

Cloudera

SEPTEMBER 11, 2018

The open data processing pipeline. IoT is expected to generate a volume and variety of data greatly exceeding what is being experienced today, requiring modernization of information infrastructure to realize value. The post Building an Open Data Processing Pipeline for IoT appeared first on Cloudera Blog.

Data Process 40

Data Process Process Building Machine Learning 40

Databand.ai

AUGUST 30, 2023

It encompasses the systems, tools, and processes that enable businesses to manage their data more efficiently and effectively. These systems typically consist of siloed data storage and processing environments, with manual processes and limited collaboration between teams.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Databand.ai

AUGUST 30, 2023

The importance of data quality cannot be overstated, as poor-quality data can result in incorrect conclusions, inefficient operations, and a lack of trust in the information provided by a company’s systems. Completeness: The extent that all required data is present and available.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

Data Engineering Weekly

FEBRUARY 25, 2024

link] Berkeley Artificial Intelligence Research (BAIR): The Shift from Models to Compound AI Systems The Berkeley AI Research (BAIR) blog discusses the evolution from focusing on single large language models (LLMs) to developing compound AI systems that integrate multiple components, including LLMs, for superior performance.

Data Engineering 62

Data Engineering Data Engineer Engineering Kafka 62

Pinterest Engineering

NOVEMBER 28, 2023

Pinterest’s real-time metrics asynchronous data processing pipeline, powering Pinterest’s time series database Goku, stood at the crossroads of opportunity. The mission was clear: identify bottlenecks, innovate relentlessly, and propel our real-time analytics processing capabilities into an era of unparalleled efficiency.

Kafka 81

Kafka Bytes Architecture Utilities 81

Netflix Tech

NOVEMBER 14, 2023

In this context, managing the data, especially when it arrives late, can present a substantial challenge! In this three-part blog post series, we introduce you to Psyberg , our incremental data processing framework designed to tackle such challenges! What is late-arriving data? Let’s dive in!

Data Engineering 105

Data Engineering Data Engineer Engineering Metadata 105

Databand.ai

AUGUST 30, 2023

DataOps , short for data operations, is an emerging discipline that focuses on improving the collaboration, integration, and automation of data processes across an organization. Accelerated Data Analytics DataOps tools help automate and streamline various data processes, leading to faster and more efficient data analytics.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

Cloudyard

APRIL 1, 2024

This blog post introduces a solution for automated warehouse size change monitoring and email alerts using Snowflake Streams and Tasks. Imagine you’re a data analyst managing a busy Snowflake account. You rely on a designated warehouse to handle your data processing needs. Create STREAM on Audit Table.

Data Pipeline 52

Data Pipeline Utilities Coding Designing 52

Data Engineering Weekly

APRIL 28, 2024

LinkedIn writes an exciting blog about what is and is not working. link] Airbnb: Airbnb Brandometer- Powering Brand Perception Measurement on Social Media Data with AI Airbnb writes about Brandometer, which utilizes AI to gauge brand perception on social media, enabling the company to measure its online reputation effectively.

Data Engineering 52

Data Engineering Data Engineer Engineering Hospitality 52

Databand.ai

JULY 19, 2023

Data Lineage Tools: Key Capabilities and 5 Notable Solutions Ryan Yackel July 19, 2023 What Are Data Lineage Tools? Data lineage tools provide a visual representation of your data’s journey across multiple systems and transformations.

Pipeline-centric 52

Pipeline-centric Data Governance Metadata Government 52

Workfall

JUNE 27, 2023

It means that there is a high risk of data loss but Apache Kafka solves this because it is distributed and can easily scale horizontally and other servers can take over the workload seamlessly. Think about it this way, traditionally, all the data needs were being handled by the backend server. Let’s get started!

Kafka 75

Kafka Architecture AWS Transportation 75

Databand.ai

JULY 19, 2023

A Beginner’s Guide [SQ] Niv Sluzki July 19, 2023 ELT is a data processing method that involves extracting data from its source, loading it into a database or data warehouse, and then later transforming it into a format that suits business needs. The extraction process requires careful planning to ensure data integrity.

Data Cleanse 52

Data Cleanse Raw Data Data Storage Data Warehouse 52

Data Engineering Weekly

JULY 30, 2023

🙌💯 Right now, we're sorting out a few snags with the registration payment system, but no stress! link] All Things Distributed: Building and operating a pretty big storage system called S3. We handle Petabytes of data!!! All our data is in S3!!! data engineer, you won’t be surprised to hear this.

Data Engineering 52

Data Engineering Data Engineer Engineering Kafka 52

Data Engineering Weekly

MAY 16, 2023

In the first part of this series, we talked about design patterns for data creation and the pros & cons of each system from the data contract perspective. In the second part, we will focus on architectural patterns to implement data quality from a data contract perspective. Why is Data Quality Expensive?

Engineering 110

Engineering Kafka Data Pipeline Data Warehouse 110

Data Engineering Weekly

SEPTEMBER 24, 2023

The blog talks about the limitations of rule engines and how LLM can enrich additional context to make the rule engine more effective. link] Sponsored: You're invited to IMPACT - The Data Observability Summit | November 8, 2023 Interested in learning how some of the best teams achieve data & AI reliability at scale?

Data Engineering 67

Data Engineering Data Engineer Engineering Kafka 67

Databand.ai

JULY 17, 2023

Automation: Implementing automated testing procedures to ensure accurate results while minimizing manual intervention during each stage of the process. The main components of MLOps include: Data management: Ensuring data quality and consistency throughout the entire ML lifecycle.

Data Pipeline 52

Data Pipeline Machine Learning High Quality Data Data Ingestion 52

Databand.ai

JUNE 8, 2023

Integrating your observability pipeline with other systems, such as incident management platforms, can further improve its effectiveness by streamlining incident response workflows. Better decision-making: Real-time insights into data processing allow for more informed decisions about resource allocation or process optimization.

Data Pipeline 52

Data Pipeline Bytes Raw Data Data Collection 52

Data Engineering Weekly

NOVEMBER 20, 2022

[link] The short YouTube video gives a nice overview of the Data Cards. We often think of AI/ ML as a complex data processing problem, but it doesn’t make any use until it is exposed to an end user or an application. The trade-off plays a critical role in a critical system like Experimentation.

Data Engineering 52

Data Engineering Data Engineer Engineering Datasets 52

Knowledge Hut

MAY 2, 2024

Apache Spark is a fast and general-purpose cluster computing system. It also supports a rich set of higher-level tools, including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming. is installed in your system. Minimum of 8 GB RAM.

Hadoop 52

Hadoop Java Scala Programming Language 52

Databand.ai

AUGUST 30, 2023

In this article: Why Is Data Testing Important? Optimizing Performance Data testing methods are also essential for optimizing the performance of data systems and applications. This results in faster, more efficient data processing, cost savings, and improved user experience.

Data Validation 52

Data Validation Data Integration Data Database 52

Netflix Tech

NOVEMBER 14, 2023

By Abhinaya Shetty , Bharath Mummadisetty This blog post will cover how Psyberg helps automate the end-to-end catchup of different pipelines, including dimension tables. In the previous installments of this series, we introduced Psyberg and delved into its core operational modes: Stateless and Stateful Data Processing.

Metadata 97

Metadata Data Pipeline Scala Data Workflow 97

Data Engineering Weekly

JUNE 18, 2023

Data management is critical for any organization to succeed in this AI world. The blog narrates LLM training options, Storage & retrieval, and the value chain to use LLM in your private data. Then start chasing where these logs are coming from the code; you know the system much better now. Kudos to the author.

Data Engineering 52

Data Engineering Data Engineer Engineering MySQL 52

Workfall

JUNE 12, 2023

So, embrace the power of Change Data Capture, and embark on a captivating journey where the magic of real-time data awaits. In this blog, we will cover: What Is CDC and Its Benefits? It bridges gaps in data ecosystems, ensuring consistency and synchronisation across systems. Where Is CDC Used and Who Uses It?

Telecommunication 80

Telecommunication Metadata Healthcare Finance 80

Netflix Tech

AUGUST 1, 2022

After evaluating the options , the team has decided to create Data Mesh as our next generation data pipeline solution. Last year we wrote a blog post about how Data Mesh helped our Studio team enable data movement use cases. This article gives an overview of the system. Processors A processor is a Flink Job.

Process 108

Process Transportation Kafka Entertainment 108

ProjectPro

JUNE 28, 2016

To explain Apache Kafka in a simple manner would be to compare it to a central nervous system than collects data from various sources. This data is constantly changing, and is voluminous. This data can be anything from clickstream data, activity/ web logs, consumer data, etc.

Kafka 40

Kafka Systems Hadoop BI 40

Data Engineering Weekly

DECEMBER 3, 2023

Github writes an excellent blog to capture the current state of the LLM integration architecture. link] Microsoft: Generative AI for Beginners Understanding Gen-AI becomes a mandatory skill for application developers and data engineers. I experienced similar drawbacks to what Lyft is talking about in Druid.

Data Engineering 70

Data Engineering Data Engineer Engineering Bytes 70

Edureka

FEBRUARY 9, 2023

Azure Data Engineering is a rapidly growing field that involves designing, building, and maintaining data processing systems using Microsoft Azure technologies. Contents: What is the role of an Azure Data Engineer? Data Modeling: Data modeling is the process of creating a conceptual representation of data.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52