WeCloudData

FEBRUARY 7, 2025

Data remains an important foundation upon which businesses innovate, develop, and thrive in the fast-paced world of technology. The data industry is booming as more and more focus is shifting towards data-driven decisions. In the data ecosystem, Data Engineering is the domain that focuses on developing infrastructures that help efficient data collection, processing, and access. […] The post Must-Have Skills for Data Engineers in 2025 appeared first on WeCloudData.

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Data Engineering Data Engineer Engineering Data Collection 52

Edureka

FEBRUARY 12, 2025

Bidirectional Encoder Representations from Transformers, or BERT, is a game-changer in the rapidly developing field of natural language processing (NLP). Built by Google, BERT revolutionizes machine learning for natural language processing, opening the door to more intelligent search engines and chatbots. The design, capabilities, and impact of BERT on altering NLP applications across industries are explored in this blog.

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IT Banking Datasets Architecture 40

Picnic Engineering

FEBRUARY 13, 2025

A decade ago, Picnic set out to reinvent grocery shopping with a tech-first, customer-centric approach. What began as a bold experiment quickly grew into a high-scale operation, powered by continuous innovation and a willingness to challenge conventions. Along the way, weve learned invaluable lessons about scaling technology, fostering culture, and driving innovation.

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Engineering Database-centric Generalist Java 52

Pinterest Engineering

FEBRUARY 11, 2025

Charles Wu, Software Engineer | Isabel Tallam, Software Engineer | Franklin Shiao, Software Engineer | Kapil Bajaj, Engineering Manager Overview Suppose you just saw an interesting rise or drop in one of your key metrics. Why did that happen? Its an easy question to ask, but much harder toanswer. One of the key difficulties in finding root causes for metric movements is that these causes can come in all shapes and sizes.

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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

Software Engineer

Monte Carlo

FEBRUARY 12, 2025

The large model is officially a commodity. In just two short years, API-based LLMs have gone from incomprehensible to smartphone accessible. The pace of AI innovation is slowing. Real world use cases are coming into focus. Going forward, the value of your genAI applications will exist solely in the fitnessand reliabilityof your own first-party data.

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The Pragmatic Engineer

FEBRUARY 6, 2025

The past six months has been something of a Doomsday scenario-esque countdown for TikTok, as the start date of its ban in the US crept ever closer. In the event, TikTok  did indeed go offline  for a few hours on 19 January, before President Trump gave the social network a stay of execution lasting 75 days. How has this uncertainty affected software engineers at the Chinese-owned social network?

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WeCloudData

FEBRUARY 12, 2025

Data scientists and Machine Learning engineers are both hot careers to follow with the recent advancement in technology. Both of these domains, data scientist vs machine learning engineer, are in high demand in any data-driven organization. Although data scientists and ML engineers share common ground in building models and handling data, they have differences in […] The post Data Scientist vs Machine Learning Engineer appeared first on WeCloudData.

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Machine Learning Engineering Data Technology 52

Monte Carlo

FEBRUARY 11, 2025

Think of your data warehouse like a well-organized library. The right setup makes finding information a breeze. The wrong one? Total chaos. Thats where data warehouse schemas come in. A data warehouse schema is a blueprint for how your data is structured and linkedusually with fact tables (for measurable data) and dimension tables (for descriptive attributes).

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Data Warehouse Electronics Retail Data 52

Confluent

FEBRUARY 12, 2025

Learn how Confluent Champion Suguna motivates her team of engineers to solve complex problems for customerswhile challenging herself to keep growing as a manager.

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Engineering Data Management 64

Snowflake

FEBRUARY 11, 2025

AI is proving that its here to stay. While 2023 brought wonder and 2024 saw widespread experimentation, 2025 will be the year that the advertising, media and entertainment industry gets serious about AI's applications. But its complicated: AI proofs of concept are graduating from the sandbox to production, just as some of AIs biggest cheerleaders are turning a bit dour.

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Apache Airflow® is the open-source standard to manage workflows as code. It is a versatile tool used in companies across the world from agile startups to tech giants to flagship enterprises across all industries. Due to its widespread adoption, Airflow knowledge is paramount to success in the field of data engineering.

Datasets

Waitingforcode

FEBRUARY 18, 2025

Using cloud managed services is often a love and hate story. On one hand, they abstract a lot of tedious administrative work to let you focus on the essentials. From another, they often have quotas and limits that you, as a data engineer, have to take into account in your daily work. These limits become even more serious when they operate in a latency-sensitive context, as the one of stream processing.

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Simon Späti

FEBRUARY 26, 2025

Geospatial data is everywhere in modern analytics. Consider this scenario: you’re a data analyst at a growing restaurant chain, and your CEO asks, “Where should we open our next location?” This seemingly simple question requires analyzing competitor locations, population density, traffic patterns, and demographicsall spatial data. Traditionally, answering this question would require expensive GIS (Geographic Information Systems) software or complex database setups.

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Jesse Anderson

FEBRUARY 11, 2025

In this episode of Unapologetically Technical, I interview Semih Salihoglu, Associate Professor at the University of Waterloo and co-founder and CEO of Kuzu. Semih is a researcher and entrepreneur with a background in distributed systems and databases. He shares his journey from a small city in Turkey to the hallowed halls of Yale University, where he studied computer science and economics.

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Analytics Vidhya

FEBRUARY 20, 2025

If you’re working with AI/ML workloads(like me) and trying to figure out which data format to choose, this post is for you. Whether you’re a student, analyst, or engineer, knowing the differences between Apache Iceberg, Delta Lake, and Apache Hudi can save you a ton of headaches when it comes to performance, scalability, and real-time […] The post Apache Iceberg vs Delta Lake vs Hudi: Best Open Table Format for AI/ML Workloads appeared first on Analytics Vidhya.

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Speaker: Tamara Fingerlin, Developer Advocate

In this new webinar, Tamara Fingerlin, Developer Advocate, will walk you through many Airflow best practices and advanced features that can help you make your pipelines more manageable, adaptive, and robust. She'll focus on how to write best-in-class Airflow DAGs using the latest Airflow features like dynamic task mapping and data-driven scheduling!

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Engineering at Meta

FEBRUARY 13, 2025

In 2024, our bug bounty program awarded more than $2.3 million in bounties, bringing our total bounties since the creation of our program in 2011 to over $20 million. As part of our defense-in-depth strategy , we continued to collaborate with the security research community in the areas of GenAI, AR/VR, ads tools, and more. We also celebrated the security research done by our bug bounty community as part of our annual bug bounty summit and many other industry events.

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Snowflake

FEBRUARY 27, 2025

Established in 2023, Snowflakes Startup Accelerator offers early-stage startups unparalleled growth opportunities through hands-on support, extensive ecosystem access and resources that surpass what other platforms provide. To further meet the needs of early-stage startups, Snowflake is expanding the Startup Accelerator to now include up to a $200 million investment in startups building industry-specific solutions and growing their businesses on the Snowflake AI Data Cloud.

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Cloud IT Amazon Web Services AWS 92

Data Engineering Weekly

FEBRUARY 18, 2025

Fluss is a compelling new project in the realm of real-time data processing. I spoke with Jark Wu , who leads the Fluss and Flink SQL team at Alibaba Cloud, to understand its origins and potential. Jark is a key figure in the Apache Flink community, known for his work in building Flink SQL from the ground up and creating Flink CDC and Fluss. You can read the Q&A version of the conversation here, and don’t forget to listen to the podcast.

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DataKitchen

FEBRUARY 17, 2025

No Python, No SQL Templates, No YAML: Why Your Open Source Data Quality Tool Should Generate 80% Of Your Data Quality Tests Automatically As a data engineer, ensuring data quality is both essential and overwhelming. The sheer volume of tables, the complexity of the data usage, and the volume of work make manual test writing an impossible task to get done.

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SQL Python Government Data Engineering 74

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Many software teams have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth. This is where Coder comes in. In our 101 Coder webinar, you’ll explore how cloud-based development environments can unlock new levels of productivity. Discover how to transition from local setups to a secure, cloud-powered ecosystem with ease.

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Netflix Tech

FEBRUARY 14, 2025

Part 1: Creating the Source of Truth for Impressions By: TulikaBhatt Imagine scrolling through Netflix, where each movie poster or promotional banner competes for your attention. Every image you hover over isnt just a visual placeholder; its a critical data point that fuels our sophisticated personalization engine. At Netflix, we call these images impressions, and they play a pivotal role in transforming your interaction from simple browsing into an immersive binge-watching experience, all tailo

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Precisely

FEBRUARY 20, 2025

Key Takeaways Trusted data is critical for AI success. Data integration ensures your AI initiatives are fueled by complete, relevant, and real-time enterprise data, minimizing errors and unreliable outcomes that could harm your business. Data integration solves key business challenges. It enables faster decision-making, boosts efficiency, and reduces costs by providing self-service access to data for AI models.

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Engineering at Meta

FEBRUARY 3, 2025

Weve previously described why we think its time to leave the leap second in the past. In todays rapidly evolving digital landscape, introducing new leap seconds to account for the long-term slowdown of the Earths rotation is a risky practice that, frankly, does more harm than good. This is particularly true in the data center space, where new protocols like Precision Time Protocol (PTP) are allowing systems to be synchronized down to nanosecond precision.

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Algorithm Utilities Systems Engineering 87

Snowflake

FEBRUARY 20, 2025

If you want to add rocket fuel to your organization, invest in employee education and training. While it may not be the first strategy that comes to mind, its one of the most effective ways to drive widespread business benefits, from increased efficiency to greater employee satisfaction and it deserves to be a top priority. Training couldnt be more relevant or pressing in our new AI normal, which is advancing at unprecedented speeds.

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Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?

Business Intelligence

Scott Logic

FEBRUARY 12, 2025

Introduction Using Playwright snapshots with mocked data can significantly improve the speed at which UI regression is carried out. It facilitates rapid automated inspection of UI elements across the three main browsers (Chromium, Firefox, Webkit). You can tie multiple assertions to one snapshot, which greatly increases efficiency for UI testing. This type of efficiency is pivotal in a rapidly scaling GUI application.

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DataKitchen

FEBRUARY 20, 2025

Announcing DataOps Data Quality TestGen 3.0: Open-Source, Generative Data Quality Software. Now With Actionable, Automatic, Data Quality Dashboards Imagine a tool that can point at any dataset, learn from your data, screen for typical data quality issues, and then automatically generate and perform powerful tests, analyzing and scoring your data to pinpoint issues before they snowball.

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Datasets Metadata Data Government 74

Start Data Engineering

FEBRUARY 3, 2025

1. Introduction 2. Split your SQL into smaller parts 2.1. Start with a baseline validation to ensure that your changes do not change the output too much 2.2. Split your CTAs/Subquery into separate functions (or models if using dbt) 2.3. Unit test your functions for maintainability and evolution of logic 3. Conclusion 4. Required reading 1. Introduction If you’ve been in the data space long enough, you would have come across really long SQL scripts that someone had written years ago.

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Precisely

FEBRUARY 26, 2025

Key Takeaways: New AI-powered innovations in the Precisely Data Integrity Suite help you boost efficiency, maximize the ROI of data investments, and make confident, data-driven decisions. These enhancements improve data accessibility, enable business-friendly governance, and automate manual processes. The Suite ensures that your business remains data-driven and competitive in a rapidly evolving landscape.

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Speaker: Jay Allardyce, Deepak Vittal, Terrence Sheflin, and Mahyar Ghasemali

As we look ahead to 2025, business intelligence and data analytics are set to play pivotal roles in shaping success. Organizations are already starting to face a host of transformative trends as the year comes to a close, including the integration of AI in data analytics, an increased emphasis on real-time data insights, and the growing importance of user experience in BI solutions.

Data

Engineering at Meta

FEBRUARY 4, 2025

Were sharing how Meta built support for data logs, which provide people with additional data about how they use our products. Here we explore initial system designs we considered, an overview of the current architecture, and some important principles Meta takes into account in making data accessible and easy to understand. Users have a variety of tools they can use to manage and access their information on Meta platforms.

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Snowflake

FEBRUARY 13, 2025

As analytics steps into the era of enterprise AI, customers requirements for a robust platform that is easy to use, connected and trusted for their current and future data needs remain unchanged. "Serverless computing" has enabled customers to use cloud capabilities without provisioning, deploying and managing either hardware or software resources.

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Monte Carlo

FEBRUARY 26, 2025

By now, most data leaders know that developing useful AI applications takes more than RAG pipelines and fine-tuned models it takes accurate, reliable, AI-ready data that you can trust in real-time. To borrow a well-worn idiom, when you put garbage data into your AI model, you get garbage results out of it. Of course, some level of data quality issues is an inevitabilityso, how bad is “bad” when it comes to data feeding your AI and ML models?

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KDnuggets

FEBRUARY 27, 2025

Looking to build your AI engineer toolkit in 2025? Here are Python libraries and frameworks you cant miss!

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With over 30 million monthly downloads, Apache Airflow is the tool of choice for programmatically authoring, scheduling, and monitoring data pipelines. Airflow enables you to define workflows as Python code, allowing for dynamic and scalable pipelines suitable to any use case from ETL/ELT to running ML/AI operations in production. This introductory tutorial provides a crash course for writing and deploying your first Airflow pipeline.

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