Start Data Engineering

DECEMBER 11, 2022

Data pipeline patterns 3.1. Multi-hop pipelines 3.3.2. Conditional/ Dynamic pipelines 3.3.3. Disconnected data pipelines 4. Source Ordering 2.3. Sink Overwritability 3. Extraction patterns 3.1.1. Time ranged 3.1.2. Full Snapshot 3.1.3. Lookback 3.1.4. Streaming 3.2. Behavioral 3.2.1. Idempotent 3.2.2.

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Data Engineering Podcast

AUGUST 13, 2023

Summary Data pipelines are the core of every data product, ML model, and business intelligence dashboard. The folks at Rivery distilled the seven principles of modern data pipelines that will help you stay out of trouble and be productive with your data.

Data Pipeline 130

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LinkedIn Engineering

JUNE 26, 2023

However, we've found that this vertical self-service model doesn't work particularly well for data pipelines, which involve wiring together many different systems into end-to-end data flows. Data pipelines power foundational parts of LinkedIn's infrastructure, including replication between data centers.

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Start Data Engineering

OCTOBER 12, 2021

Introduction Testing your data pipeline 1. Data quality testing 3. Unit and contract testing Conclusion Further reading Introduction Testing data pipelines are different from testing other applications, like a website backend. End-to-end system testing 2. Monitoring and alerting 4.

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KDnuggets

NOVEMBER 2, 2023

For production grade LLM apps, you need a robust data pipeline. This article talks about the different stages of building a Gen AI data pipeline and what is included in these stages.

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Confessions of a Data Guy

JANUARY 13, 2023

Rust has been on my mind a lot lately, probably because of Data Engineering boredom, watching Spark clusters chug along like some medieval farm worker endlessly trudging through the muck and mire of life. appeared first on Confessions of a Data Guy.

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Start Data Engineering

SEPTEMBER 16, 2021

Introduction Choosing tools/frameworks to scale your data pipelines can be confusing. If you have struggled with Data pipelines that randomly crash Finding guides on how to scale your data pipelines from the ground up Then this post is for you. Choose your scaling strategy 5. Conclusion 6. Further reading 7.

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Start Data Engineering

MAY 13, 2021

What is an idempotent function Pre-requisites Why idempotency matters Making your data pipeline idempotent Conclusion Further reading References What is an idempotent function “Idempotence is the property of certain operations in mathematics and computer science whereby they can be applied multiple times without changing the result beyond the (..)

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Towards Data Science

APRIL 3, 2023

Data pipeline management done right simplifies deployment and increases the availability and accessibility of data for analytics Continue reading on Towards Data Science »

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Precisely

DECEMBER 28, 2023

Business success is based on how we use continuously changing data. That’s where streaming data pipelines come into play. This article explores what streaming data pipelines are, how they work, and how to build this data pipeline architecture. What is a streaming data pipeline?

Data Pipeline 91

Data Pipeline Building Kafka Big Data 91

Snowflake

APRIL 17, 2024

Since the previous Python connector API mostly communicated via SQL, it also hindered the ability to manage Snowflake objects natively in Python, restricting data pipeline efficiency and the ability to complete complex tasks. Or, experience these features firsthand at our free Dev Day event on June 6th in the Demo Zone.

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Data Pipeline Python Data Engineering Data Engineer 108

Snowflake

JUNE 4, 2024

Snowflake’s new Python API (GA soon) simplifies data pipelines and is readily available through pip install snowflake. Additionally, Dynamic Tables are a new table type that you can use at every stage of your processing pipeline. Interact with Snowflake objects directly in Python. Automate or code, the choice is yours.

Data Pipeline 100

Data Pipeline Python SQL Government 100

Confessions of a Data Guy

MARCH 1, 2023

The post GitHub’s CoPilot Writes Data Pipelines appeared first on Confessions of a Data Guy.

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Data Pipeline Data 100

Databand.ai

JUNE 28, 2023

Data Pipeline Observability: A Model For Data Engineers Eitan Chazbani June 29, 2023 Data pipeline observability is your ability to monitor and understand the state of a data pipeline at any time. We believe the world’s data pipelines need better data observability.

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Snowflake

OCTOBER 3, 2023

We are excited to announce the availability of data pipelines replication, which is now in public preview. In the event of an outage, this powerful new capability lets you easily replicate and failover your entire data ingestion and transformations pipelines in Snowflake with minimal downtime.

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Data Pipeline Management Data Ingestion Data 86

François Nguyen

FEBRUARY 28, 2021

Today, we are going to apply these principles to the data pipelines. The idea is to transpose these 7 principles to data pipeline knowing that Data pipelines are 100% flexible : if you have the skills, you build the pipeline you want. How does a bad data pipeline process look like ?

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Start Data Engineering

NOVEMBER 11, 2021

Writing an end-to-end data pipeline test 4. Introduction Data pipelines can have multiple software components. If you are wondering What is the best way to end-to-end test data pipelines? In this post, we go over some techniques that you can use to set up end-to-end tests for your data pipelines.

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Data Pipeline Cloud Data 130

Picnic Engineering

FEBRUARY 8, 2023

In our previous blog, Dima Kalashnikov explained how we configure our Internal services pipeline in the Analytics Platform. In this post, we will explain how our team automates the creation of new data pipeline deployments. Now, we can have a pipeline ready in minutes.

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Data Pipeline Kafka Data Architecture 105

Start Data Engineering

APRIL 26, 2021

Using distributed frameworks Pros & Cons Conclusion Further reading References Introduction If you are Wondering how to write memory efficient data pipelines in python Working with a dataset that is too large to fit into memory Then this post is for you.

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KDnuggets

SEPTEMBER 5, 2023

Build a streaming data pipeline using Formula 1 data, Python, Kafka, RisingWave as the streaming database, and visualize all the real-time data in Grafana.

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Data Pipeline Kafka Building Python 114

Hevo

JUNE 7, 2024

Data pipelines play a vital role in modern data management. They connect various data sources within an organization to those who need them. The ability to move data quickly and efficiently allows for identifying patterns and new insights to aid in long-term planning and daily decision-making.

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Confluent

DECEMBER 2, 2022

A data pipeline is the process of data movement and transformation from its source to destination. Learn types of data pipelines and how they’re used.

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Data Pipeline Data Process IT 98

Data Science Blog: Data Engineering

MAY 20, 2024

Continuous Integration and Continuous Delivery (CI/CD) for Data Pipelines: It is a Game-Changer with AnalyticsCreator! The need for efficient and reliable data pipelines is paramount in data science and data engineering. They transform data into a consistent format for users to consume.

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Monte Carlo

FEBRUARY 8, 2024

Just as a watchmaker meticulously adjusts every tiny gear and spring in harmonious synchrony for flawless performance, modern data pipeline optimization requires a similar level of finesse and attention to detail. Learn how cost, processing speed, resilience, and data quality all contribute to effective data pipeline optimization.

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Data Pipeline AWS Datasets Data 52

Ascend.io

APRIL 26, 2023

Data teams worldwide are building data pipelines with the point solutions that make up the “modern data stack.” Pipelines built with this approach are slow and require constant manual reprogramming and updates. For a deep dive into this level of automation, take a look at our What Is Data Pipeline Automation paper.

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Start Data Engineering

DECEMBER 12, 2021

Introduction If you are building data pipelines from the ground up, the number of available data engineering tools to choose from can be overwhelming. Introduction 2. Requirements 3. Components 4. Choosing tools 4.1 Requirement x Component framework 4.2 Conclusion 6. Further reading 1.

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Start Data Engineering

APRIL 8, 2021

Understand the current state of the data pipeline 1. Timelines & deliverables Deliver iteratively Conclusion Further reading References Introduction As data engineers, you will have to re-engineer legacy data pipelines. Think like the end user 2. Know the why 3. End user interviews 4. Reduce the scope 5.

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Data Engineering Podcast

SEPTEMBER 11, 2022

Building reliable data pipelines is a complex and costly undertaking with many layered requirements. In order to reduce the amount of time and effort required to build pipelines that power critical insights Manish Jethani co-founded Hevo Data. Data stacks are becoming more and more complex.

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Ascend.io

MARCH 17, 2023

These engineering functions are almost exclusively concerned with data pipelines, spanning ingestion, transformation, orchestration, and observation — all the way to data product delivery to the business tools and downstream applications. Pipelines need to grow faster than the cost to run them.

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Ascend.io

MARCH 17, 2023

These engineering functions are almost exclusively concerned with data pipelines, spanning ingestion, transformation, orchestration, and observation — all the way to data product delivery to the business tools and downstream applications. Pipelines need to grow faster than the cost to run them.

Data Pipeline 52

Data Pipeline Datasets Data Software Engineer 52

Ascend.io

AUGUST 22, 2023

The demands of building, scaling, and maintaining data pipelines have grown increasingly complex and error-prone. Data engineers are now drowning in repetitive tasks, aspiring to drive data-backed decisions. Traditional approaches to building these pipelines have showcased their vulnerabilities.

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Hevo

JUNE 7, 2024

Data integration enables you to back up your data and use it for analysis when necessary, positively impacting your business. Snowflake data pipelines can help you streamline data flow into your […]

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Data Engineering Podcast

OCTOBER 28, 2021

In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Get started for free at dataengineeringpodcast.com/hightouch. Can you describe what Decodable is and the story behind it?

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Ascend.io

JANUARY 2, 2024

But let’s be honest, creating effective, robust, and reliable data pipelines, the ones that feed your company’s reporting and analytics, is no walk in the park. From building the connectors to ensuring that data lands smoothly in your reporting warehouse, each step requires a nuanced understanding and strategic approach.

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Data Pipeline Building Raw Data Data Warehouse 52

Towards Data Science

MARCH 3, 2024

Data engineering techniques for robust and sustainable ETL Continue reading on Towards Data Science »

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Confluent

SEPTEMBER 26, 2023

Confluent's Q3 '23 Launch brings intelligent, secure, cost-effective data pipelines with cloud-native Apache Flink, enterprise clusters powered by Kora engine, and more.

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Data Pipeline Data Cloud Engineering 66

Towards Data Science

SEPTEMBER 10, 2023

Example feature #2: Auto-healing jobs As most data platform people can confirm, things are always changing in their data pipelines. Two very popular scenarios are: data size growing over time, and code changes. An idempotent pipeline would also be ideal here in case many iterations are needed.

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