Analytics Vidhya

FEBRUARY 17, 2023

Introduction Are you curious about the latest advancements in the data tech industry? Perhaps you’re hoping to advance your career or transition into this field. In that case, we invite you to check out DataHour, a series of webinars led by experts in the field.

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ODSC - Open Data Science

FEBRUARY 24, 2023

These tools will help make your initial data exploration process easy. ydata-profiling GitHub | Website The primary goal of ydata-profiling is to provide a one-line Exploratory Data Analysis (EDA) experience in a consistent and fast solution. Output is a fully self-contained HTML application. You can watch it on demand here.

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Analytics Vidhya

JUNE 28, 2022

This article was published as a part of the Data Science Blogathon. “Preponderance data opens doorways to complex and Avant analytics.” ” Introduction to SQL Queries Data is the premium product of the 21st century.

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Flipboard

AUGUST 17, 2023

Amazon Redshift is the most popular cloud data warehouse that is used by tens of thousands of customers to analyze exabytes of data every day. AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, ML, and application development.

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ODSC - Open Data Science

AUGUST 23, 2023

Run pandas at scale on your data warehouse Most enterprise data teams store their data in a database or data warehouse, such as Snowflake, BigQuery, or DuckDB. Ponder solves this problem by translating your pandas code to SQL that can be understood by your data warehouse.

Data Warehouse 52

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Pickl AI

NOVEMBER 4, 2024

Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering? Data Engineering is designing, constructing, and managing systems that enable data collection, storage, and analysis. ETL is vital for ensuring data quality and integrity. from 2025 to 2030.

Data Engineer 52

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The MLOps Blog

MARCH 15, 2023

In this post, you will learn about the 10 best data pipeline tools, their pros, cons, and pricing. A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process.

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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. Below are the best practices.

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Pickl AI

AUGUST 1, 2023

The primary goal of Data Engineering is to transform raw data into a structured and usable format that can be easily accessed, analyzed, and interpreted by Data Scientists, analysts, and other stakeholders. Future of Data Engineering The Data Engineering market will expand from $18.2

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Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

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Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

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ODSC - Open Data Science

JANUARY 30, 2024

So let’s do a quick overview of the job of data engineer, and maybe you might find a new interest. Building and maintaining data pipelines Data integration is the process of combining data from multiple sources into a single, consistent view. Think of data engineers as the architects of the data ecosystem.

Data Engineer 52

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Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Alation

JANUARY 17, 2023

The modern data stack is a combination of various software tools used to collect, process, and store data on a well-integrated cloud-based data platform. 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.

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Mlearning.ai

MARCH 15, 2023

Build a Stocks Price Prediction App powered by Snowflake, AWS, Python and Streamlit — Part 2 of 3 A comprehensive guide to develop machine learning applications from start to finish. Introduction Welcome Back, Let's continue with our Data Science journey to create the Stock Price Prediction web application.

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The MLOps Blog

JANUARY 26, 2024

Reference table for which technologies to use for your FTI pipelines for each ML system. Related article How to Build ETL Data Pipelines for ML See also MLOps and FTI pipelines testing Once you have built an ML system, you have to operate, maintain, and update it. All of them are written in Python.

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IBM Journey to AI blog

SEPTEMBER 19, 2023

To pursue a data science career, you need a deep understanding and expansive knowledge of machine learning and AI. Your skill set should include the ability to write in the programming languages Python, SAS, R and Scala. And you should have experience working with big data platforms such as Hadoop or Apache Spark.

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Pickl AI

OCTOBER 17, 2024

It is a data integration process that involves extracting data from various sources, transforming it into a suitable format, and loading it into a target system, typically a data warehouse. ETL is the backbone of effective data management, ensuring organisations can leverage their data for informed decision-making.

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IBM Journey to AI blog

NOVEMBER 3, 2023

When you make it easier to work with events, other users like analysts and data engineers can start gaining real-time insights and work with datasets when it matters most. As a result, you reduce the skills barrier and increase your speed of data processing by preventing important information from getting stuck in a data warehouse.

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phData

FEBRUARY 14, 2023

Data integration is essentially the Extract and Load portion of the Extract, Load, and Transform (ELT) process. Data ingestion involves connecting your data sources, including databases, flat files, streaming data, etc, to your data warehouse. Snowflake provides native ways for data ingestion.

Data Warehouse 59

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Pickl AI

JUNE 7, 2024

Also Read: Top 10 Data Science tools for 2024. It is a process for moving and managing data from various sources to a central data warehouse. This process ensures that data is accurate, consistent, and usable for analysis and reporting. This process helps organisations manage large volumes of data efficiently.

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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. Savings may vary depending on configurations, workloads and vendor.

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ODSC - Open Data Science

JANUARY 18, 2024

This individual is responsible for building and maintaining the infrastructure that stores and processes data; the kinds of data can be diverse, but most commonly it will be structured and unstructured data. They’ll also work with software engineers to ensure that the data infrastructure is scalable and reliable.

Data Engineer 52

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Iguazio

JANUARY 22, 2024

Feedback - ​Collect production data, metadata, and metrics to tune the model and application further, and to enable governance and explainability. The data pipeline - Takes the data from different sources (document, databases, online, data warehouses, etc.), This helps cleanse the data.

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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.

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phData

OCTOBER 17, 2024

It even offers a user-friendly interface to visualize the pipelines and monitor progress. Airflow is entirely in Python, so it’s relatively easy for those with some Python experience to get started using it. Aside from migrations, Data Source is also great for data quality checks and can generate data pipelines.

AI 52

AI AI SQL Data Quality 52

Snorkel AI

JANUARY 24, 2023

Snowpark, which is Snowflake’s developer framework that extends the benefits of the Data Cloud beyond SQL to Python, Scala, and Java, can be used to scale batch inference across your Snowflake data warehouse. Models built in Snorkel Flow can be registered on Snowflake as Snowpark UDFs.

AI 52

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Snorkel AI

JANUARY 24, 2023

Snowpark, which is Snowflake’s developer framework that extends the benefits of the Data Cloud beyond SQL to Python, Scala, and Java, can be used to scale batch inference across your Snowflake data warehouse. Models built in Snorkel Flow can be registered on Snowflake as Snowpark UDFs.

AI 52

AI AI ML ML 52

ODSC - Open Data Science

FEBRUARY 15, 2024

With an exploration of real-world data, this session will equip you with the knowledge to immediately retrain better models. These systems represent data as knowledge graphs and implement graph traversal algorithms to help find content in massive datasets.

Data Engineer 52

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phData

AUGUST 22, 2024

Consider a data pipeline that detects its own failures, diagnoses the issue, and recommends the fix—all automatically. This is the potential of self-healing pipelines, and this blog explores how to implement them using dbt, Snowflake Cortex , and GitHub Actions. python/requirements.txt - name: Trigger dbt job run: | python -u./python/run_monitor_and_self_heal.py

SQL 52

SQL Data Quality Python Data Warehouse 52

Pickl AI

SEPTEMBER 5, 2024

These skills enable professionals to leverage Azure’s cloud technologies effectively and address complex data challenges. Below are the essential skills required for thriving in this role: Programming Proficiency: Expertise in languages such as Python or R for coding and data manipulation.

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Pickl AI

DECEMBER 9, 2024

Hive is a data warehouse tool built on Hadoop that enables SQL-like querying to analyse large datasets. What is the Difference Between Structured and Unstructured Data? Structured data is organised in tabular formats like databases, while unstructured data, such as images or videos, lacks a predefined format.

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DagsHub

AUGUST 23, 2024

It is particularly popular among data engineers as it integrates well with modern data pipelines (e.g., Source: [link] Monte Carlo is a code-free data observability platform that focuses on data reliability across data pipelines. quality) for your data.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

Pickl AI

JULY 3, 2023

Explore their features, functionalities, and best practices for creating reports, dashboards, and visualizations. Develop programming skills: Enhance your programming skills, particularly in languages commonly used in BI development such as SQL, Python, or R.

Business Intelligence 52

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The MLOps Blog

JANUARY 23, 2023

Dolt LakeFS Delta Lake Pachyderm Git-like versioning Database tool Data lake Data pipelines Experiment tracking Integration with cloud platforms Integrations with ML tools Examples of data version control tools in ML DVC Data Version Control DVC is a version control system for data and machine learning teams.

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The MLOps Blog

MARCH 15, 2023

ETL usually stands for “Extract, Transform and Load,” and it refers to a process in data warehousing. Sourcing the data In our case, the data was provided by our client, which was a product-based organization. The data pipelines can be scheduled as event-driven or be run at specific intervals the users choose.

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Alation

JANUARY 26, 2023

That’s why many organizations invest in technology to improve data processes, such as a machine learning data pipeline. However, data needs to be easily accessible, usable, and secure to be useful — yet the opposite is too often the case.

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