Databand.ai

JUNE 8, 2023

Observability in Your Data Pipeline: A Practical Guide Eitan Chazbani June 8, 2023 Achieving observability for data pipelines means that data engineers can monitor, analyze, and comprehend their data pipeline’s behavior. This is part of a series of articles about data observability.

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Towards Data Science

APRIL 6, 2023

Today’s post follows the same philosophy: fitting local and cloud pieces together to build a data pipeline. And, when it comes to data engineering solutions, it’s no different: They have databases, ETL tools, streaming platforms, and so on — a set of tools that makes our life easier (as long as you pay for them). not sponsored.

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Monte Carlo

AUGUST 17, 2023

Data pipelines are a set of processes that enable the flow of data from one system to another, and one such process you can use is ETL ( e xtract, t ransform, l oad). The way to think of the relationship between the two is that ETL is a data pipeline, but not all data pipelines are ETL.

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Team Data Science

MAY 10, 2020

I'll use Python and Spark because they are the top 2 requested skills in Toronto. Kafka, while not in the top 5 most in demand skills, was still the most requested buffer technology requested which makes it worthwhile to include it.

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Netflix Tech

NOVEMBER 14, 2023

Understanding the nature of the late-arriving data and processing requirements will help decide which pattern is most appropriate for a use case. Stateful Data Processing : This pattern is useful when the output depends on a sequence of events across one or more input streams.

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Cloudera

MARCH 14, 2023

In this blog post we will put these capabilities in context and dive deeper into how the built-in, end-to-end data flow life cycle enables self-service data pipeline development. Key requirements for building data pipelines Every data pipeline starts with a business requirement.

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Cloudera

JUNE 2, 2021

Data pipelines are in high demand in today’s data-driven organizations. As critical elements in supplying trusted, curated, and usable data for end-to-end analytic and machine learning workflows, the role of data pipelines is becoming indispensable.

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Striim

NOVEMBER 17, 2023

Real-time data processing in the world of machine learning allows data scientists and engineers to focus on model development and monitoring. Striim’s strength lies in its capacity to connect to over 150 data sources, enabling real-time data acquisition from virtually any location and simplifying data transformations.

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Grouparoo

OCTOBER 26, 2021

As a result, data has to be moved between the source and destination systems and this is usually done with the aid of data pipelines. What is a Data Pipeline? A data pipeline is a set of processes that enable the movement and transformation of data from different sources to destinations.

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ProjectPro

DECEMBER 7, 2021

Data pipelines are a significant part of the big data domain, and every professional working or willing to work in this field must have extensive knowledge of them. Table of Contents What is a Data Pipeline? The Importance of a Data Pipeline What is an ETL Data Pipeline?

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Ascend.io

APRIL 12, 2023

From this research, we developed a framework with a sequence of stages to implement data integrity quickly and measurably via data pipelines. Table of Contents Why does data integrity matter? At every level of a business, individuals must trust the data, so they can confidently make timely decisions. Let’s explore!

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DataKitchen

NOVEMBER 4, 2021

The typical pharmaceutical organization faces many challenges which slow down the data team: Raw, barely integrated data sets require engineers to perform manual , repetitive, error-prone work to create analyst-ready data sets. Cloud computing has made it much easier to integrate data sets, but that’s only the beginning.

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Netflix Tech

NOVEMBER 14, 2023

In this three-part blog post series, we introduce you to Psyberg , our incremental data processing framework designed to tackle such challenges! We’ll discuss batch data processing, the limitations we faced, and how Psyberg emerged as a solution. Let’s dive in!

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Marc Lamberti

SEPTEMBER 22, 2023

Stemming from this analogy, “you” is the orchestrator in data orchestration, and the recipe is the data pipeline. It was created in 2014 by Airbnb and has since been widely adopted by the data engineering community, primarily as it was the first orchestrator allowing to author data pipelines programmatically.

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Netflix Tech

NOVEMBER 14, 2023

In the previous installments of this series, we introduced Psyberg and delved into its core operational modes: Stateless and Stateful Data Processing. Now, let’s explore the state of our pipelines after incorporating Psyberg. This data pipeline monitors the various stages in the customer lifecycle.

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Hevo

MAY 30, 2023

The healthcare industry has seen an exponential growth in the use of data management and integration tools in recent years to leverage the data at their disposal. Unlocking the potential of “Big Data” is imperative in enhancing patient care quality, streamlining operations, and allocating resources optimally.

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Netflix Tech

DECEMBER 14, 2023

Engineers from across the company came together to share best practices on everything from Data Processing Patterns to Building Reliable Data Pipelines. The result was a series of talks which we are now sharing with the rest of the Data Engineering community!

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Data Engineering Weekly

MAY 16, 2023

I won’t bore you with the importance of data quality in the blog. Instead, Let’s examine the current data pipeline architecture and ask why data quality is expensive. Instead of looking at the implementation of the data quality frameworks, Let's examine the architectural patterns of the data pipeline.

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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.

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Christophe Blefari

APRIL 19, 2024

Fast News ⚡️ Theseus against really big data ( credits ) Principal Engineer — Although staffs and principals have been on the career ladder for a long time, there are very few articles on what it takes to become one of the greats. This article covers the whole ladder and the mix of skills needed to reach the top.

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Ascend.io

DECEMBER 19, 2023

That’s what we call a data pipeline. It could just as well be ‘ELT for Snowflake’ The key takeaway is that these terms are representative of the actual activity being undertaken: the construction and management of data pipelines within the Snowflake environment.

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Monte Carlo

FEBRUARY 12, 2024

Monte Carlo has been the leader in data observability to improve the data reliability of structured and unstructured data processed by modern data platforms built around warehouses, lakes, and lakehouses. The category was introduced with five original pillars: Freshness : Did the data arrive when expected?

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Knowledge Hut

MARCH 20, 2024

The job description for Azure data engineer that I have elucidated below focuses more on foundational tasks while providing opportunities for learning and growth within the field: Data ingestion: This role involves assisting in the process of collecting and importing data from various sources into Azure storage solutions.

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Ascend.io

APRIL 22, 2024

The Evolution of Data Engineering Over the past decade, we’ve seen tremendous change in the data engineering landscape. As companies scale their data and analytics capabilities, their data pipelines and operations have grown increasingly complex. Most of this data is never collected, and lost forever.

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Ascend.io

OCTOBER 12, 2023

This could range from speeding up data entry processes, to ensuring data consistency, to near real-time data analysis. Whether it’s a 20% reduction in data processing time or a 15% increase in data accuracy, having measurable outcomes can guide your journey.

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Knowledge Hut

MARCH 15, 2024

The job description for Azure data engineer that I have elucidated below focuses more on foundational tasks while providing opportunities for learning and growth within the field: Data ingestion: This role involves assisting in the process of collecting and importing data from various sources into Azure storage solutions.

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Data Engineering Data Engineer Engineering Data Governance 52

Ascend.io

AUGUST 31, 2023

In this article, we assess: The role of the data warehouse on one hand, and the data lake on the other; The features of ETL and ELT in these two architectures; The evolution to EtLT; The emerging role of data pipelines. Let’s take a closer look. Enterprises have an opportunity to undergo a metamorphosis.

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Christophe Blefari

JANUARY 20, 2024

He wrote some years ago 3 articles defining data engineering field. Some concepts When doing data engineering you can touch a lot of different concepts. Then here a list of global resources that can help you navigate through the field: The Data Engineer Roadmap — An image with advices and technology names to watch.

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Netflix Tech

MARCH 10, 2023

Hence we built the data pipeline that can be used to extract the existing assets metadata and process it specifically to each new use case. Production Use Cases Real-Time APIs (backed by the Cassandra database) for asset metadata access don’t fit analytics use cases by data science or machine learning teams.

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Data Engineering Podcast

JANUARY 30, 2022

Matt Harrison is a Python expert with a long history of working with data who now spends his time on consulting and training. Today’s episode is Sponsored by Prophecy.io – the low-code data engineering platform for the cloud. The only thing worse than having bad data is not knowing that you have it.

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Databand.ai

AUGUST 30, 2023

It emphasizes the importance of collaboration between different teams, such as data engineers, data scientists, and business analysts, to ensure that everyone has access to the right data at the right time. This includes data ingestion, processing, storage, and analysis.

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Knowledge Hut

JUNE 26, 2023

From exploratory data analysis (EDA) and data cleansing to data modeling and visualization, the greatest data engineering projects demonstrate the whole data process from start to finish. Data pipeline best practices should be shown in these initiatives. Source Code: Yelp Review Analysis 2.

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Databand.ai

JULY 17, 2023

By adopting a set of best practices inspired by Agile methodologies, DevOps principles, and statistical process control techniques, DataOps helps organizations deliver high-quality data insights more efficiently. However, if machine learning models are at the core of your business operations, MLOps will provide better support.

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Engineering at Meta

DECEMBER 19, 2023

Workflows to productionize ML models include data pipelines to get the information needed to train the models, training workflows to build and improve the models over time, evaluation systems to test the models, and inference workflows to actually use the models in Meta’s products. Model to feature isolation. label imbalance).

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Databand.ai

AUGUST 30, 2023

Slow data processing: Due to the manual nature of many data workflows in legacy architectures, data processing can be time-consuming and resource-intensive. A DataOps architecture must consider the performance, scalability, and cost implications of the chosen data storage platform.

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Knowledge Hut

NOVEMBER 2, 2023

An Azure Data Engineer is a professional responsible for designing, implementing, and managing data solutions using Microsoft's Azure cloud platform. They work with various Azure services and tools to build scalable, efficient, and reliable data pipelines, data storage solutions, and data processing systems.

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Databand.ai

JULY 19, 2023

This capability is particularly useful in complex data landscapes, where data may pass through multiple systems and transformations before reaching its final destination Impact analysis: When changes are made to data sources or data processing systems, it’s critical to understand the potential impact on downstream processes and reports.

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