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After 10 years of Data Engineering work, I think it’s time to hang up the proverbial hat and ride off into the sunset, never to be seen again. I wish. Everything has changed in 10 years, yet nothing has changed in 10 years, how is that even possible? Sometimes I wonder if I’ve learned anything […] The post What I’ve Learned After A Decade Of Data Engineering appeared first on Confessions of a Data Guy.
Image by author Model deployment is the process of trained models being integrated into practical applications. This includes defining the necessary environment, specifying how input data is introduced into the model and the output produced, and the capacity to analyze new data and provide relevant predictions or categorizations.
The telecom industry is undergoing an unprecedented transformation. Fueled by tech advancements such as 5G, cloud computing, Internet of Things (IoT) and machine learning (ML), telecoms have the opportunity to reshape and streamline operations and make significant improvements in service delivery, customer experience and network optimization. Key to these technologies is generative AI (gen AI), a dynamic form of artificial intelligence that leverages vast amounts of data to analyze and produce r
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Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.
In the age of AI, enterprises are increasingly looking to extract value from their data at scale but often find it difficult to establish a scalable data engineering foundation that can process the large amounts of data required to build or improve models. Designed for processing large data sets, Spark has been a popular solution, yet it is one that can be challenging to manage, especially for users who are new to big data processing or distributed systems.
In the age of AI, enterprises are increasingly looking to extract value from their data at scale but often find it difficult to establish a scalable data engineering foundation that can process the large amounts of data required to build or improve models. Designed for processing large data sets, Spark has been a popular solution, yet it is one that can be challenging to manage, especially for users who are new to big data processing or distributed systems.
A summary of sessions at the first Data Engineering Open Forum at Netflix on April 18th, 2024 The Data Engineering Open Forum at Netflix on April 18th, 2024. At Netflix, we aspire to entertain the world, and our data engineering teams play a crucial role in this mission by enabling data-driven decision-making at scale. Netflix is not the only place where data engineers are solving challenging problems with creative solutions.
Recent Snowflake workshops and roundtables have started with the question: “Does your data strategy match your AI ambition?” It certainly sparks customer engagement, but is that the right question to ask? Right now, it seems appropriate with all of the interest — dare I say “hype” — around AI. But it merely reflects the current darling of the tech world, focusing on the technology itself, rather than the ultimate goal.
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Speaker: Alex Salazar, CEO & Co-Founder @ Arcade | Nate Barbettini, Founding Engineer @ Arcade | Tony Karrer, Founder & CTO @ Aggregage
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Introduction In Lounge by Zalando, we run an Elasticsearch cluster in Kubernetes to store user facing article descriptions. Our business model is such that we receive about three times the normal load during the busy hour in the morning and therefore we use schedules to automatically scale in and out applications to handle that peak. If scaling out in the morning fails, we face a potential catastrophe.
AWS (Amazon Web Services) is one of the leading providers of Cloud Services. It provides Cloud services like Amazon Redshift, Amazon S3, and many others for Data Storage. Extract, Transform, Load are 3 important steps performed in the field of Data Warehousing and Databases.
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Introduction The purpose of this post is to introduce you to Oracle Streams concepts. You will learn about the various components that make up the Oracle Streams technology. Towards the end, you will find practical examples detailing how to implement Oracle Streams CDC in a production environment. What is Oracle Streams?
Speaker: Andrew Skoog, Founder of MachinistX & President of Hexis Representatives
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As data grows at a massive scale, industries are adopting new ways to manage data effectively. One of the most popular techniques for managing data is CDC. The benefits of change data capture (CDC) enables organizations to capture changes made to data sources.
Businesses today are overflowing with data and thus are majorly dependent on big data platforms that support digital transformation through which they can streamline the flow of data for real-time insights delivery and better decision making. This article will take you through some of the important aspects of Snowflake security and sharing practices.
In recent years, businesses worldwide have scaled up their Data Collection operations, leading to the term ‘Big Data.’ Today, companies collect information from various sources, including Business Transactions, Industrial Equipment, Social Media, and more. Accordingly, these organizations need an efficient way of storing and analyzing this information.
Businesses today are overflowing with data and thus are majorly dependent on big data platforms that support digital transformation through which they can streamline the flow of data for real-time insights delivery and better decision making. This article will take you through some of the important aspects of Snowflake security and sharing practices.
With Airflow being the open-source standard for workflow orchestration, knowing how to write Airflow DAGs has become an essential skill for every data engineer. This eBook provides a comprehensive overview of DAG writing features with plenty of example code. You’ll learn how to: Understand the building blocks DAGs, combine them in complex pipelines, and schedule your DAG to run exactly when you want it to Write DAGs that adapt to your data at runtime and set up alerts and notifications Scale you
The surge in Big Data and Cloud Computing has created a huge demand for real-time Data Analytics. Companies rely on complex ETL (Extract Transform and Load) Pipelines that collect data from sources in the raw form and deliver it to a storage destination in a form suitable for analysis.
As data grows at a massive scale, industries are adopting new ways to manage data effectively. One of the most popular techniques for managing data is CDC. The benefits of change data capture (CDC) enables organizations to capture changes made to data sources.
AWS (Amazon Web Services) is one of the leading providers of Cloud Services. It provides Cloud services like Amazon Redshift, Amazon S3, and many others for Data Storage. Extract, Transform, Load are 3 important steps performed in the field of Data Warehousing and Databases.
As data collection within organizations proliferates rapidly, developers are automating data movement through Data Ingestion techniques. However, implementing complex Data Ingestion techniques can be tedious and time-consuming for developers.
Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage
When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m
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Introduction The purpose of this post is to introduce you to Oracle Streams concepts. You will learn about the various components that make up the Oracle Streams technology. Towards the end, you will find practical examples detailing how to implement Oracle Streams CDC in a production environment. What is Oracle Streams?
Debezium is a distributed, open-sourced platform for tracking real-time changes in databases. It is called an event streaming platform as it converts data changes on databases into events, and when such changes are accessed by different applications to process the information further.
Data loading is a surmountable task for organizations all over the world. While several platforms make this task easier, several data loading issues surface regularly. Amazon’s Redshift is a popular choice for data loading in an organized manner. In this blog, you will learn how to perform Redshift Incremental Load.
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