Data Engineering Weekly

NOVEMBER 26, 2023

🕵️‍♂️📈 Before we delve into the patterns, it's important to remember that data infrastructure maturity model. Revolution in AI: Large Language Models Take the World by Storm I’m not certainly talking about OpenAI boardroom drama. But I had my doubts. The categories are merging.

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Cloudera

OCTOBER 30, 2023

The public cloud is increasingly becoming the preferred platform to host data analytics – related projects, such as business intelligence, machine learning (ML), and AI applications. Cloudera FinOps Capabilities CDP is a cloud-native platform helping companies accelerate cloud adoption to run their data and analytics workloads.

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Cloudera

JULY 19, 2022

They must try their best to prevent financial crime, and as with many other initiatives the advancements in fighting financial crime are found in maturing the use of data and AI. . Rules are informed by models that can be adjusted dynamically when new fraud schemes arise. appeared first on Cloudera Blog.

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Cloudera

APRIL 9, 2021

This is part 4 in this blog series. This blog series follows the manufacturing and operations data lifecycle stages of an electric car manufacturer – typically experienced in large, data-driven manufacturing companies. The second blog dealt with creating and managing Data Enrichment pipelines. Here are the key stages: .

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DataKitchen

MAY 10, 2021

As organizations strive to become more data-driven, Forrester recommends 5 actions to take to move from one stage of insights-driven business maturity to another. . The following resources will help you understand DataOps principles and how to get started: Blog: For Data Team Success, What You Do is Less Important Than How You Do It.

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Data Engineering Weekly

APRIL 21, 2023

Ananth and Aswin discussed a blog from BuzzFeed that shares lessons learned from building products powered by generative AI. The blog highlights how generative AI can be integrated into a company's work culture and workflow to enhance creativity rather than replace jobs. Welcome to another episode of Data Engineering Weekly Radio.

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Cloudera

MARCH 29, 2022

With that in place, government IT leaders can then apply the kinds of rich data analytics that produce copious benefits, including more informed decision-making, greater transparency, greater efficiency, and reduced costs. Hybrid cloud is the Model of Choice.

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DataKitchen

SEPTEMBER 20, 2021

The pipelines and workflows that ingest data, process it and output charts, dashboards, or other analytics resemble a production pipeline. According to a recent Gartner survey, data teams spend only 22% of their time on “data innovation, data monetization and enhanced analytics insights.” Figure 1: The four phases of Lean DataOps.

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Data Engineering Weekly

MARCH 5, 2023

link] Meta: Four Analytics Best Practices We Adopted — and Why You Should Too Meta writes about four analytical best practices to ensure the most trustworthy and responsible data-driven decisions across the company. A key highlight of the blog Their data team leveraged a cost-effective geolocation API to solve the problem.

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Cloudera

JANUARY 5, 2023

As more industries mature digitally and widely adopt AI and machine learning technologies, 2023 will be a pivotal year for organizations looking to deploy emerging tech solutions company-wide to fulfill business objectives. 2- Operationalizing adaptive AI systems for quicker business decision-making.

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Data Engineering Weekly

APRIL 16, 2023

The blog narrates the difference between deterministic and non-deterministic system design and the challenges of building LLM applications in production. link] The flow control in the LLM application is an exciting read, and a generalized programming model will emerge soon. I print this out and read it a couple of times.

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Data Engineering Weekly

AUGUST 20, 2023

The blog narrates the potential possibilities of LLM’s impact in each stage of the data warehouse. link] Sponsored: Great Data Debate–The State of Data Mesh Since 2019, the data mesh has woven itself into every blog post, event presentation, and webinar. But 4 years later, in 2023 — where has the data mesh gotten us?

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Data Engineering Data Engineer Engineering Data 52

Snowflake

MAY 4, 2023

The market volatility we’ve seen in recent years has only underscored the value of using data to model and analyze market behavior and avoid to manage risk for the Street. Given our mature data landscape, the attractiveness of Snowflake Native Apps is that they allow us to leverage compute capabilities directly where the data already lives.

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Cloudera

AUGUST 3, 2021

Advanced analytics empower risk reduction . Advanced analytics and enterprise data are empowering several overarching initiatives in supply chain risk reduction – improved visibility and transparency into all aspects of the supply chain balanced with data governance and security. . Improve Visibility within Supply Chains.

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Cloudera

JULY 27, 2021

Digital ecosystem design continues to evolve in different geographies and under alternate regulatory environments, but common challenges across service providers center on API structures and business models, where the Forum offers an opportunity to come together. Edge analytics by definition require in-network deployment.

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DataKitchen

AUGUST 19, 2021

Each distinct phase of the drug lifecycle requires a unique focus for analytics. As generic alternatives become available, the market enters the maturity phase where cost efficiency and margins become most important. The analytics team is under tremendous pressure during the early phases of the drug’s lifetime.

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Snowflake

AUGUST 21, 2023

In this blog post, we’ll review the core data mesh principles, highlight how both organizations and modern data platforms are putting those principles into action, and demonstrate just how achievable a secure and efficient data mesh architecture can be.

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Cloudera

JUNE 7, 2022

And, as Deloitte put it: “Hybrid cloud is the de facto model”. . Leading industry analysts rated Cloudera better at analytic and operational data use cases than many well-known cloud vendors. And we were cited by those same analysts as rating better than all vendors for hybrid and multi-cloud analytic solutions.

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Cloudera

JANUARY 28, 2022

As I meet with our customers, there are always a range of discussions regarding the use of the cloud for financial services data and analytics. All in a distributed cloud model that spans multi-public, private & edge clouds. . appeared first on Cloudera Blog. Orchestration and management of automated workflows .

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DoorDash Engineering

FEBRUARY 15, 2023

When this process works well, it can be an amazing experience to work on a product development team, ship ML models to production, and make them 1% better every day. Once we identify an opportunity, we usually start small by testing the ML hypothesis using a heuristic before deciding to invest into building out the ML models.

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Food Machine Learning Architecture Building 72

phData: Data Engineering

JULY 12, 2022

Within data engineering , one of the most frequent tasks is modeling data into data marts and data products. Traditionally, if a company wanted to model some data, there were two types of resources available that could accomplish this: data engineers or data analysts. What Is Data Modeling?

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Cloudera

SEPTEMBER 9, 2021

Cloudera Machine Learning or Cloudera Data Warehouse), to deliver fast data and analytics to downstream components. Infrastructure cost optimization by converting a fixed cost structure that previously consisted of infrastructure and cloud subscription costs per node into a variable cost model in the cloud based on actual consumption.

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Cloudera

MARCH 25, 2021

Probably more well known, the EU’s GDPR serves as a model for much of the new data privacy legislation coming forward. Risk management can be optimized by the improved use of data and analytics to run models, account for more variables and scrutinize probable outcomes. appeared first on Cloudera Blog.

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Insurance Banking Data Governance Government 66

Confluent

FEBRUARY 6, 2019

It takes much more effort than just building an analytic model with Python and your favorite machine learning framework. Uber expanded Michelangelo “to serve any kind of Python model from any source to support other Machine Learning and Deep Learning frameworks like PyTorch and TensorFlow [instead of just using Spark for everything].”.

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Machine Learning Python Kafka Java 107

Silectis

FEBRUARY 17, 2021

In this blog post, we’ll walk you through how data science and data engineering are complementary disciplines. This post delineates how data science, data analytics, and data engineering build towards the ultimate goal of empowering data-driven decisions. Esoteric modelling techniques by data scientists?

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DataKitchen

OCTOBER 28, 2021

We have the tools to create data analytics workflows that address AI bias. When our work processes for creating and monitoring analytics contain built-in controls against bias, data analytics organizations will no longer be dependent on individual social awareness or heroism. What Is AI Bias?

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Cloudera

AUGUST 31, 2021

Data warehousing is the backbone of every data driven organization , providing mission critical analytics. Today, modern data warehousing has evolved to meet the intensive demands of the newest analytics required for a business to be data driven. Are you looking to include log, semi-structured, or sensor data in your analytics?

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Monte Carlo

AUGUST 15, 2022

Over the past few decades, I’ve built or led data teams at enterprise organizations and startups across industries, from healthcare and cutting-edge SaaS, to geospatial analytics, and back again. Is data flowing somewhere that you can manage it from an analytics perspective? Do you just have an operational database?

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Cloudera

AUGUST 22, 2021

And yet I know that cloud adoption, particularly a hybrid multi-cloud model, is the way forward in navigating headwinds like global supply chain disruptions, Covid lockdowns and giving businesses a competitive edge to propel them to unimagined levels. I’d say the main differences lie in the individual market and industry nuances.

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Cloud Telecommunication Insurance Manufacturing 103

Maxime Beauchemin

JANUARY 20, 2017

They’re highly analytical, and are interested in data visualization. Unlike data scientists — and inspired by our more mature parent, software engineering  — data engineers build tools, infrastructure, frameworks, and services. In retrospect, complex SCD modeling techniques are not intuitive and reduce accessibility.

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Cloudera

AUGUST 2, 2018

It is a reality – leading banks perform hundreds of analytical experiments every day on their massive data sets to find the next bit of information about their customers. Data teams in these insurance firms are leading the charge in rebuilding entire business models around data and analytics.

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Data Engineering Podcast

APRIL 14, 2024

It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. NoSQL is a generalized term that encompasses a number of different data models. How does the underlying representation (e.g.

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Data Engineering Podcast

APRIL 21, 2024

It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. What are the ways that the advent and evolution of language models have influenced your product roadmap? When is Shortwave the wrong choice?

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Netflix Tech

OCTOBER 19, 2020

In our previous blog post we introduced Edgar, our troubleshooting tool for streaming sessions. Our tactical approach was to use Netflix-specific libraries for collecting traces from Java-based streaming services until open source tracer libraries matured. —?which is difficult when troubleshooting distributed systems.

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Cloudera

MARCH 2, 2018

Banking, transportation, healthcare, retail, and real estate, all have seen the emergence of new business models fundamentally changing how customers use their services. In the nonprofit sector, early applications of data analytics and machine learning have mostly focused on improving fundraising and marketing.

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Hospitality Healthcare Machine Learning Transportation 43

DataKitchen

OCTOBER 2, 2023

Data Teams and Their Types of Data Journeys In the rapidly evolving landscape of data management and analytics, data teams face various challenges ranging from data ingestion to end-to-end observability. Journeys provide a context for understanding and observing complex elements across and down a data analytic system.

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dbt Developer Hub

NOVEMBER 28, 2021

represents the culmination of over five years of development and refinement to the analytics engineering experience - smoothing off sharp edges, speeding up workflows and enabling whole new classes of work. and is relatively mature. As we get closer to dbt v1.0 The project uses dbt v0.16.0 to 0.17.2,

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