Hacker News

JULY 18, 2024

ABOUT EVENTUAL Eventual is a data platform that helps data scientists and engineers build data applications across ETL, analytics and ML/AI. OUR PRODUCT IS OPEN-SOURCE AND USED AT ENTERPRISE SCALE Our distributed data engine Daft [link] is open-sourced and runs on 800k CPU cores daily.

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Pickl AI

OCTOBER 17, 2024

Summary: This article explores the significance of ETL Data in Data Management. It highlights key components of the ETL process, best practices for efficiency, and future trends like AI integration and real-time processing, ensuring organisations can leverage their data effectively for strategic decision-making.

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AWS Machine Learning Blog

FEBRUARY 21, 2025

Data exploration and model development were conducted using well-known machine learning (ML) tools such as Jupyter or Apache Zeppelin notebooks. Apache Hive was used to provide a tabular interface to data stored in HDFS, and to integrate with Apache Spark SQL. This created a challenge for data scientists to become productive.

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Applied Data Science

AUGUST 2, 2021

Team Building the right data science team is complex. With a range of role types available, how do you find the perfect balance of Data Scientists , Data Engineers and Data Analysts to include in your team? The Data Engineer Not everyone working on a data science project is a data scientist.

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Mlearning.ai

JULY 8, 2023

In this article we’re going to check what is an Azure function and how we can employ it to create a basic extract, transform and load (ETL) pipeline with minimal code. Extract, transform and Load Before we begin, let’s shed some light on what an ETL pipeline essentially is. ELT stands for extract, load and transform.

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Pickl AI

APRIL 6, 2023

Accordingly, one of the most demanding roles is that of Azure Data Engineer Jobs that you might be interested in. The following blog will help you know about the Azure Data Engineering Job Description, salary, and certification course. How to Become an Azure Data Engineer?

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AWS Machine Learning Blog

JANUARY 10, 2024

Specialist Data Engineering at Merck, and Prabakaran Mathaiyan, Sr. ML Engineer at Tiger Analytics. The large machine learning (ML) model development lifecycle requires a scalable model release process similar to that of software development. This post is co-written with Jayadeep Pabbisetty, Sr.

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Flipboard

DECEMBER 11, 2024

Organizations are building data-driven applications to guide business decisions, improve agility, and drive innovation. Many of these applications are complex to build because they require collaboration across teams and the integration of data, tools, and services.

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Mlearning.ai

MAY 16, 2023

Data engineering is a rapidly growing field that designs and develops systems that process and manage large amounts of data. There are various architectural design patterns in data engineering that are used to solve different data-related problems.

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AWS Machine Learning Blog

FEBRUARY 21, 2025

Previously, he was a Data & Machine Learning Engineer at AWS, where he worked closely with customers to develop enterprise-scale data infrastructure, including data lakes, analytics dashboards, and ETL pipelines. He specializes in designing, building, and optimizing large-scale data solutions.

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AWS Machine Learning Blog

OCTOBER 9, 2024

Amazon Lookout for Metrics is a fully managed service that uses machine learning (ML) to detect anomalies in virtually any time-series business or operational metrics—such as revenue performance, purchase transactions, and customer acquisition and retention rates—with no ML experience required. To learn more, see the documentation.

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The MLOps Blog

SEPTEMBER 7, 2023

This situation is not different in the ML world. Data Scientists and ML Engineers typically write lots and lots of code. Building a mental model for ETL components Learn the art of constructing a mental representation of the components within an ETL process.

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AWS Machine Learning Blog

JUNE 18, 2024

Despite the challenges, Afri-SET, with limited resources, envisions a comprehensive data management solution for stakeholders seeking sensor hosting on their platform, aiming to deliver accurate data from low-cost sensors. This happens only when a new data format is detected to avoid overburdening scarce Afri-SET resources.

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AWS Machine Learning Blog

JANUARY 5, 2024

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models.

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IBM Journey to AI blog

MARCH 14, 2024

Db2 Warehouse fully supports open formats such as Parquet, Avro, ORC and Iceberg table format to share data and extract new insights across teams without duplication or additional extract, transform, load (ETL). This allows you to scale all analytics and AI workloads across the enterprise with trusted data. 

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AWS Machine Learning Blog

NOVEMBER 29, 2023

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. Amazon SageMaker notebook jobs allow data scientists to run their notebooks on demand or on a schedule with a few clicks in SageMaker Studio.

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

ML operationalization summary As defined in the post MLOps foundation roadmap for enterprises with Amazon SageMaker , ML and operations (MLOps) is the combination of people, processes, and technology to productionize machine learning (ML) solutions efficiently.

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The MLOps Blog

DECEMBER 7, 2022

And we at deployr , worked alongside them to find the best possible answers for everyone involved and build their Data and ML Pipelines. Building data and ML pipelines: from the ground to the cloud It was the beginning of 2022, and things were looking bright after the lockdown’s end.

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Becoming Human

JANUARY 23, 2023

After understanding data science let’s discuss the second concern “ Data Science vs AI ”. So, we know that data science is a process of getting insights from data and helps the business but where this Artificial Intelligence (AI) lies? So, it looks like magic but it’s not magic. If we talk about AI.

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The MLOps Blog

JANUARY 23, 2023

However, there are some key differences that we need to consider: Size and complexity of the data In machine learning, we are often working with much larger data. Basically, every machine learning project needs data. Given the range of tools and data types, a separate data versioning logic will be necessary.

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The MLOps Blog

MARCH 15, 2023

This includes the tools and techniques we used to streamline the ML model development and deployment processes, as well as the measures taken to monitor and maintain models in a production environment. Costs: Oftentimes, cost is the most important aspect of any ML model deployment. This includes data quality, privacy, and compliance.

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phData

FEBRUARY 25, 2025

Over the years, businesses have increasingly turned to Snowflake AI Data Cloud for various use cases beyond just data analytics and business intelligence. From data engineering and machine learning to real-time data processing, Snowflake has become a central hub for organizations seeking to unify and leverage their data at scale.

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phData

FEBRUARY 7, 2024

On the client side, Snowpark consists of libraries, including the DataFrame API and native Snowpark machine learning (ML) APIs for model development (public preview) and deployment (private preview). Machine Learning Training machine learning (ML) models can sometimes be resource-intensive.

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Mlearning.ai

FEBRUARY 9, 2023

Working as a Data Scientist — Expectation versus Reality! 11 key differences in 2023 Photo by Jan Tinneberg on Unsplash Working in Data Science and Machine Learning (ML) professions can be a lot different from the expectation of it. In courses/projects, it is common to have data available.

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Alation

MAY 24, 2022

Alation has been leading the evolution of the data catalog to a platform for data intelligence. Higher data intelligence drives higher confidence in everything related to analytics and AI/ML. The Lineage & Dataflow API is a good example enabling customers to add ETL transformation logic to the lineage graph.

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Alation

APRIL 4, 2023

The people navigating these increasingly chaotic landscapes need a single place to find, understand, and use data with total confidence. Expanded Integration with Databricks Unity Catalog Unity Catalog is Databricks ’ governance and admin layer for all lakehouse data and AI assets, including files, tables, ML models, and dashboards.

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ODSC - Open Data Science

APRIL 3, 2023

Skills like effective verbal and written communication will help back up the numbers, while data visualization (specific frameworks in the next section) can help you tell a complete story. Data Wrangling: Data Quality, ETL, Databases, Big Data The modern data analyst is expected to be able to source and retrieve their own data for analysis.

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IBM Journey to AI blog

JULY 17, 2023

is our enterprise-ready next-generation studio for AI builders, bringing together traditional machine learning (ML) and new generative AI capabilities powered by foundation models. Watsonx.data allows customers to augment data warehouses such as Db2 Warehouse and Netezza and optimize workloads for performance and cost. IBM watsonx.ai

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DagsHub

OCTOBER 23, 2024

Managing unstructured data is essential for the success of machine learning (ML) projects. Without structure, data is difficult to analyze and extracting meaningful insights and patterns is challenging. This article will discuss managing unstructured data for AI and ML projects. What is Unstructured Data?

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The MLOps Blog

MARCH 28, 2023

And because it takes more than technologies and processes to succeed with MLOps, he will also share details on: 1 Brainly’s ML use cases, 2 MLOps culture, 3 Team structure, 4 And technologies Brainly uses to deliver AI services to its clients, Enjoy the article! Multiple AI teams also contribute to ML infrastructure initiatives.

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phData

SEPTEMBER 4, 2024

ThoughSpot can easily connect to top cloud data platforms such as Snowflake AI Data Cloud , Oracle, SAP HANA, and Google BigQuery. In that case, ThoughtSpot also leverages ELT/ETL tools and Mode, a code-first AI-powered data solution that gives data teams everything they need to go from raw data to the modern BI stack.

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ODSC - Open Data Science

OCTOBER 9, 2024

So what are you waiting for? Get your pass today !

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phData

JUNE 7, 2023

The most critical and impactful step you can take towards enterprise AI today is ensuring you have a solid data foundation built on the modern data stack with mature operational pipelines, including all your most critical operational data. This often involves software engineering, data engineering, and system design skills.

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phData

SEPTEMBER 27, 2024

If the event log is your customer’s diary, think of persistent staging as their scrapbook – a place where raw customer data is collected, organized, and kept for future reference. In traditional ETL (Extract, Transform, Load) processes in CDPs, staging areas were often temporary holding pens for data.

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Flipboard

NOVEMBER 22, 2024

Organizations run millions of Apache Spark applications each month to prepare, move, and process their data for analytics and machine learning (ML). During development, data engineers often spend hours sifting through log files, analyzing execution plans, and making configuration changes to resolve issues.

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Flipboard

DECEMBER 18, 2023

Customers use Amazon Redshift as a key component of their data architecture to drive use cases from typical dashboarding to self-service analytics, real-time analytics, machine learning (ML), data sharing and monetization, and more. Discover how you can use Amazon Redshift to build a data mesh architecture to analyze your data.

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Women in Big Data

MARCH 5, 2025

I had the pleasure of interviewing Anu Jekal , the CEO of Data Surge , a leading company in data and AI/ML. At Women in Big Data (WiBD), Anu has been a mentor and volunteer for almost 2 years. My career started as an operations engineer, where I quickly learned Linux the hard way. Q: Tell me more about Data Surge?

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