Data Science Dojo

MAY 10, 2023

Data Engineer Data engineers are responsible for building, maintaining, and optimizing data infrastructures. They require strong programming skills, expertise in data processing, and knowledge of database management.

Data Science 366

Data Science Data Scientist Database Administration Machine Learning 366

Towards AI

MARCH 21, 2023

Navigating the World of Data Engineering: A Beginner’s Guide. A GLIMPSE OF DATA ENGINEERING ❤ IMAGE SOURCE: BY AUTHOR Data or data? No matter how you read or pronounce it, data always tells you a story directly or indirectly. Data engineering can be interpreted as learning the moral of the story.

Data Engineering 98

Data Engineering Data Engineering Data Engineering Data Engineer 98

Hacker News

NOVEMBER 19, 2024

Here are a few of the things that you might do as an AI Engineer at TigerEye: - Design, develop, and validate statistical models to explain past behavior and to predict future behavior of our customers’ sales teams - Own training, integration, deployment, versioning, and monitoring of ML components - Improve TigerEye’s existing metrics collection and (..)

Computer Science 147

Computer Science Computer Science ML ML 147

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.

ETL 52

ETL Azure Python Internet of Things 52

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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ML ML Python ETL 126

Data Science Dojo

MARCH 15, 2023

It is designed to assist data engineers in transforming, converting, and validating data in a simplified manner while ensuring accuracy and reliability. The Meltano CLI can efficiently handle complex data engineering tasks, providing a user-friendly interface that simplifies the ELT process.

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Azure Data Science Data Engineering Data Engineer 195

The MLOps Blog

MAY 17, 2023

However, efficient use of ETL pipelines in ML can help make their life much easier. This article explores the importance of ETL pipelines in machine learning, a hands-on example of building ETL pipelines with a popular tool, and suggests the best ways for data engineers to enhance and sustain their pipelines.

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ETL Data Pipeline ML ML 59

Towards AI

JANUARY 28, 2024

To start, get to know some key terms from the demo: Snowflake: The centralized source of truth for our initial data Magic ETL: Domo’s tool for combining and preparing data tables ERP: A supplemental data source from Salesforce Geographic: A supplemental data source (i.e., Instagram) used in the demo Why Snowflake?

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ETL Python Database Data Preparation 111

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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Azure Data Engineering Data Engineering Data Engineering 52

Pickl AI

JULY 25, 2023

Unfolding the difference between data engineer, data scientist, and data analyst. Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. Read more to know.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

ODSC - Open Data Science

JANUARY 18, 2024

Data engineering is a rapidly growing field, and there is a high demand for skilled data engineers. If you are a data scientist, you may be wondering if you can transition into data engineering. In this blog post, we will discuss how you can become a data engineer if you are a data scientist.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

phData

JANUARY 5, 2023

Python is the top programming language used by data engineers in almost every industry. Python has proven proficient in setting up pipelines, maintaining data flows, and transforming data with its simple syntax and proficiency in automation. Why Connect Snowflake to Python?

Python 52

Python Data Engineering Data Engineering Data Engineering 52

Pickl AI

JUNE 7, 2024

Summary: Choosing the right ETL tool is crucial for seamless data integration. Top contenders like Apache Airflow and AWS Glue offer unique features, empowering businesses with efficient workflows, high data quality, and informed decision-making capabilities. Choosing the right ETL tool is crucial for smooth data management.

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ETL Data Quality Data Pipeline Data Warehouse 40

Data Science Dojo

JULY 3, 2024

Data science bootcamps are intensive short-term educational programs designed to equip individuals with the skills needed to enter or advance in the field of data science. They cover a wide range of topics, ranging from Python, R, and statistics to machine learning and data visualization.

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Data Science Machine Learning Machine Learning Data Visualization 150

DataSeries

AUGUST 15, 2024

Enrich data engineering skills by building problem-solving ability with real-world projects, teaming with peers, participating in coding challenges, and more. Globally several organizations are hiring data engineers to extract, process and analyze information, which is available in the vast volumes of data sets.

Data Engineering 40

Data Engineering Data Engineering Data Engineering Data Engineer 40

Applied Data Science

MAY 6, 2021

To keep myself sane, I use Airflow to automate tasks with simple, reusable pieces of code for frequently repeated elements of projects, for example: Web scraping ETL Database management Feature building and data validation And much more! Note that we can use the core python package datetime to help us define our DAGs.

Data Scientist 130

Data Scientist Python Data Science Database 130

AWS Machine Learning Blog

JUNE 18, 2024

The solution harnesses the capabilities of generative AI, specifically Large Language Models (LLMs), to address the challenges posed by diverse sensor data and automatically generate Python functions based on various data formats. The solution only invokes the LLM for new device data file type (code has not yet been generated).

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AWS Python AI AI 113

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.

Data Science 130

Data Science Data Scientist ML ML 130

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. Choose the plus sign and for Notebook , choose Python 3.

SQL 160

SQL AWS Data Lakes AI 160

AWS Machine Learning Blog

FEBRUARY 21, 2025

This setup uses the AWS SDK for Python (Boto3) to interact with AWS services. 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.

AWS 104

AWS Natural Language Processing Machine Learning Machine Learning 104

phData

SEPTEMBER 2, 2024

Best practices are a pivotal part of any software development, and data engineering is no exception. This ensures the data pipelines we create are robust, durable, and secure, providing the desired data to the organization effectively and consistently. What Are Matillion Jobs and Why Do They Matter?

ETL 52

ETL Data Warehouse SQL Database 52

The MLOps Blog

SEPTEMBER 7, 2023

Data Scientists and ML Engineers typically write lots and lots of code. From writing code for doing exploratory analysis, experimentation code for modeling, ETLs for creating training datasets, Airflow (or similar) code to generate DAGs, REST APIs, streaming jobs, monitoring jobs, etc.

Machine Learning 52

Machine Learning Machine Learning ETL ML 52

phData

FEBRUARY 7, 2024

Snowpark is the set of libraries and runtimes in Snowflake that securely deploy and process non-SQL code, including Python, Java, and Scala. On the server side, runtimes include Python, Java, and Scala in the warehouse model or Snowpark Container Services (public preview).

Python 52

Python ML ML SQL 52

AWS Machine Learning Blog

NOVEMBER 29, 2023

You can use this notebook job step to easily run notebooks as jobs with just a few lines of code using the Amazon SageMaker Python SDK. Data scientists currently use SageMaker Studio to interactively develop their Jupyter notebooks and then use SageMaker notebook jobs to run these notebooks as scheduled jobs.

ML 94

ML ML Data Scientist Python 94

phData

AUGUST 9, 2024

Snowflake can not natively read files on these services, so an ETL service is needed to upload the data. Once an ETL process is set up, it is easy for users to break the pipeline by adding fields or modifying the source file in unexpected ways. ETL applications are often expensive and require some level of expertise to run.

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ETL Data Warehouse Data Governance Tableau 52

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.

Analytics 52

Analytics Analytics Data Analyst Data Science 52

The MLOps Blog

MARCH 15, 2023

Our activities mostly revolved around: 1 Identifying data sources 2 Collecting & Integrating data 3 Developing Analytical/ML models 4 Integrating the above into a cloud environment 5 Leveraging the cloud to automate the above processes 6 Making the deployment robust & scalable Who was involved in the project?

AWS 52

AWS ETL ML ML 52

Alation

JANUARY 17, 2023

It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse. Data ingestion/integration services. Reverse ETL tools. Data orchestration tools. A Note on the Shift from ETL to ELT. Data scientists.

Data Warehouse 52

Data Warehouse ETL Tableau Cloud Data 52

ODSC - Open Data Science

OCTOBER 18, 2024

Set specific, measurable targets Data science goals to “increase sales” lack the clarity needed to evaluate success and secure ongoing funding. Audit existing data assets Inventory internal datasets, ETL capabilities, past analytical initiatives, and available skill sets.

Data Science 52

Data Science Data Scientist Analytics Analytics 52

Tableau

AUGUST 21, 2023

Finally, Tableau allows you to create custom territories using Tableau groups and overlay data with demographic information, giving you a comprehensive view of your data. To really unlock the full potential of your data, you need to be able to modify it with familiar tools before you analyze it in Tableau.

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Tableau Database Analytics Analytics 97

AWS Machine Learning Blog

SEPTEMBER 18, 2024

Airflow for workflow orchestration Airflow schedules and manages complex workflows, defining tasks and dependencies in Python code. An example direct acyclic graph (DAG) might automate data ingestion, processing, model training, and deployment tasks, ensuring that each step is run in the correct order and at the right time.

AWS 106

AWS Machine Learning Machine Learning ML 106

ODSC - Open Data Science

DECEMBER 9, 2024

The Widespread Adoption of Open DataScience The use of open source data science tools has absolutely explodedwere talking a whopping 650% growth over the past five years. Additionally, a clear majority of current projects ( 85% to be exact) leverage open-source programming languages like Python and R rather than proprietary options.

Data Science 40

Data Science Machine Learning Machine Learning Python 40

IBM Journey to AI blog

JULY 17, 2023

Within watsonx.ai, users can take advantage of open-source frameworks like PyTorch, TensorFlow and scikit-learn alongside IBM’s entire machine learning and data science toolkit and its ecosystem tools for code-based and visual data science capabilities.

AI 74

AI AI Machine Learning Machine Learning 74

phData

APRIL 18, 2024

Now that the topic is created let’s generate some data. Instead of manually generating data or using programming languages like Python, we will use a datagen connector already installed as part of the initial configuration. Create the topic with all default settings and a single partition.

Clustering 52

Clustering Data Engineering Data Engineer Data Engineering 52

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.

Analytics 52

Analytics Analytics SQL ETL 52

The MLOps Blog

DECEMBER 7, 2022

May be useful Best Workflow and Pipeline Orchestration Tools: Machine Learning Guide Phase 1—Data pipeline: getting the house in order Once the dust was settled, we got the Architecture Canvas completed, and the plan was clear to everyone involved, the next step was to take a closer look at the architecture. What’s in the box?

ML 52

ML ML AWS ETL 52