Flipboard

JUNE 22, 2023

Data engineering startup Prophecy is giving a new turn to data pipeline creation. Known for its low-code SQL tooling, the California-based company today announced data copilot, a generative AI assistant that can create trusted data pipelines from natural language prompts and improve pipeline quality …

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Analytics Vidhya

SEPTEMBER 22, 2022

This article was published as a part of the Data Science Blogathon. Introduction When creating data pipelines, Software Engineers and Data Engineers frequently work with databases using Database Management Systems like PostgreSQL.

Database 326

Database Data Pipeline Data Engineering Data Engineer 326

Data Science Dojo

JANUARY 27, 2023

Data Science Dojo is offering Airbyte for FREE on Azure Marketplace packaged with a pre-configured web environment enabling you to quickly start the ELT process rather than spending time setting up the environment. Free to use. Conclusion  There are a ton of small services that aren’t supported on traditional data pipeline platforms.

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Analytics Vidhya

OCTOBER 3, 2024

Introduction Managing a data pipeline, such as transferring data from CSV to PostgreSQL, is like orchestrating a well-timed process where each step relies on the previous one. Apache Airflow streamlines this process by automating the workflow, making it easy to manage complex data tasks.

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IBM Journey to AI blog

MAY 15, 2024

Data engineers build data pipelines, which are called data integration tasks or jobs, as incremental steps to perform data operations and orchestrate these data pipelines in an overall workflow. With a multicloud data strategy, organizations need to optimize for data gravity and data locality.

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AWS Machine Learning Blog

AUGUST 8, 2024

Managing and retrieving the right information can be complex, especially for data analysts working with large data lakes and complex SQL queries. This tool converts questions from data analysts asked in natural language (such as “Which table contains customer address information?”)

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Data Science Dojo

SEPTEMBER 11, 2024

Let’s explore each of these components and its application in the sales domain: Synapse Data Engineering: Synapse Data Engineering provides a powerful Spark platform designed for large-scale data transformations through Lakehouse. Here, we changed the data types of columns and dealt with missing values.

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AWS Machine Learning Blog

DECEMBER 6, 2023

To overcome these limitations, we propose a solution that combines RAG with metadata and entity extraction, SQL querying, and LLM agents, as described in the following sections. Typically, these analytical operations are done on structured data, using tools such as pandas or SQL engines.

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Data Science Dojo

JULY 6, 2023

Data engineering tools are software applications or frameworks specifically designed to facilitate the process of managing, processing, and transforming large volumes of data. Spark offers a rich set of libraries for data processing, machine learning, graph processing, and stream processing.

Data Engineering 195

Data Engineering Data Engineering Data Engineer Data Engineering 195

Flipboard

NOVEMBER 27, 2024

While customers can perform some basic analysis within their operational or transactional databases, many still need to build custom data pipelines that use batch or streaming jobs to extract, transform, and load (ETL) data into their data warehouse for more comprehensive analysis. Choose Delete stack.

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Pickl AI

JULY 8, 2024

Summary: This blog explains how to build efficient data pipelines, detailing each step from data collection to final delivery. Introduction Data pipelines play a pivotal role in modern data architecture by seamlessly transporting and transforming raw data into valuable insights.

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IBM Data Science in Practice

JANUARY 13, 2025

The blog post explains how the Internal Cloud Analytics team leveraged cloud resources like Code-Engine to improve, refine, and scale the data pipelines. Background One of the Analytics teams tasks is to load data from multiple sources and unify it into a data warehouse. Thus, it has only a minimal footprint.

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Data Science Connect

JANUARY 27, 2023

Data engineering is a crucial field that plays a vital role in the data pipeline of any organization. It is the process of collecting, storing, managing, and analyzing large amounts of data, and data engineers are responsible for designing and implementing the systems and infrastructure that make this possible.

Data Engineering 130

Data Engineering Data Engineering Data Engineer Data Engineering 130

phData

AUGUST 6, 2024

As today’s world keeps progressing towards data-driven decisions, organizations must have quality data created from efficient and effective data pipelines. For customers in Snowflake, Snowpark is a powerful tool for building these effective and scalable data pipelines.

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Data Pipeline Python Data Engineering Data Engineer 52

AWS Machine Learning Blog

MARCH 14, 2024

Using structured data to answer questions requires a way to effectively extract data that’s relevant to a user’s query. We formulated a text-to-SQL approach where by a user’s natural language query is converted to a SQL statement using an LLM. The SQL is run by Amazon Athena to return the relevant data.

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SQL AWS AI AI 129

AWS Machine Learning Blog

OCTOBER 24, 2024

A provisioned or serverless Amazon Redshift data warehouse. Basic knowledge of a SQL query editor. Implementation steps Load data to the Amazon Redshift cluster Connect to your Amazon Redshift cluster using Query Editor v2. For this post we’ll use a provisioned Amazon Redshift cluster. A SageMaker domain.

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Smart Data Collective

SEPTEMBER 8, 2021

A lot of Open-Source ETL tools house a graphical interface for executing and designing Data Pipelines. It can be used to manipulate, store, and analyze data of any structure. It generates Java code for the Data Pipelines instead of running Pipeline configurations through an ETL Engine.

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phData

JUNE 14, 2023

which play a crucial role in building end-to-end data pipelines, to be included in your CI/CD pipelines. Each migration SQL script is assigned a unique sequence number to facilitate the correct order of application. Additionally, we need to incorporate Flyway variables into the Flyway configuration file.

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Data Pipeline Database SQL Data Engineering 52

Towards AI

FEBRUARY 29, 2024

Apache Kafka plays a crucial role in enabling data processing in real-time by efficiently managing data streams and facilitating seamless communication between various components of the system. Apache Kafka Apache Kafka is a distributed event streaming platform used for building real-time data pipelines and streaming applications.

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Apache Kafka SQL Clustering Data Pipeline 142

phData

JULY 12, 2023

Unlike traditional methods that rely on complex SQL queries for orchestration, Matillion Jobs provides a more streamlined approach. By converting SQL scripts into Matillion Jobs , users can take advantage of the platform’s advanced features for job orchestration, scheduling, and sharing. In our case, this table is “orders.”

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SQL ETL Database Data Pipeline 52

phData

APRIL 21, 2023

Unlike traditional methods that rely on complex SQL queries for orchestration, Matillion Jobs provide a more streamlined approach. By converting SQL scripts into Matillion Jobs , users can take advantage of the platform’s advanced features for job orchestration, scheduling, and sharing. In our case, this table is “orders.”

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SQL ETL Database Data Pipeline 52

Applied Data Science

AUGUST 2, 2021

Automation Automating data pipelines and models ➡️ 6. The most common data science languages are Python and R   —  SQL is also a must have skill for acquiring and manipulating data. 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

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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SQL ML ML Python 52

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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SQL ML ML Python 52

ODSC - Open Data Science

FEBRUARY 17, 2023

Cloud Computing, APIs, and Data Engineering NLP experts don’t go straight into conducting sentiment analysis on their personal laptops. Data Engineering Platforms Spark is still the leader for data pipelines but other platforms are gaining ground. Knowing some SQL is also essential.

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Deep Learning Deep Learning Data Science Natural Language Processing 111

Data Science Dojo

JULY 3, 2024

Data Processing and Analysis : Techniques for data cleaning, manipulation, and analysis using libraries such as Pandas and Numpy in Python. Databases and SQL : Managing and querying relational databases using SQL, as well as working with NoSQL databases like MongoDB.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

Tableau

JUNE 8, 2021

Domain experts, for example, feel they are still overly reliant on core IT to access the data assets they need to make effective business decisions. In all of these conversations there is a sense of inertia: Data warehouses and data lakes feel cumbersome and data pipelines just aren't agile enough.

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phData

JULY 4, 2024

Because it runs Snowflake SQL from an easy-to-use, code-first GUI interface, it can take advantage of everything Snowflake offers, even if the feature is brand new. This blog will cover creating customized nodes in Coalesce, what new advanced features can already be used as nodes, and how to create them as part of your data pipeline.

SQL 52

SQL Data Pipeline Data Engineering Data Engineering 52

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

AUGUST 8, 2024

Putting the T for Transformation in ELT (ETL) is essential to any data pipeline. After extracting and loading your data into the Snowflake AI Data Cloud , you may wonder how best to transform it. Luckily, Snowflake answers this question with many features designed to transform your data for all your analytic use cases.

SQL 52

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Flipboard

AUGUST 17, 2023

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. You can use query_string to filter your dataset by SQL and unload it to Amazon S3.

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ML ML AWS Data Warehouse 123

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 Visualization: Matplotlib, Seaborn, Tableau, etc.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Analytics Vidhya

FEBRUARY 20, 2023

Introduction Azure data factory (ADF) is a cloud-based data ingestion and ETL (Extract, Transform, Load) tool. The data-driven workflow in ADF orchestrates and automates data movement and data transformation.

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ODSC - Open Data Science

FEBRUARY 2, 2023

Computer Science and Computer Engineering Similar to knowing statistics and math, a data scientist should know the fundamentals of computer science as well. While knowing Python, R, and SQL are expected, you’ll need to go beyond that. Big Data As datasets become larger and more complex, knowing how to work with them will be key.

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Data Science Data Scientist Computer Science Computer Science 98

Smart Data Collective

SEPTEMBER 26, 2021

The raw data can be fed into a database or data warehouse. An analyst can examine the data using business intelligence tools to derive useful information. . To arrange your data and keep it raw, you need to: Make sure the data pipeline is simple so you can easily move data from point A to point B.

Database 130

Database Data Visualization Big Data Big Data 130

Dataconomy

MAY 16, 2023

Key skills and qualifications for machine learning engineers include: Strong programming skills: Proficiency in programming languages such as Python, R, or Java is essential for implementing machine learning algorithms and building data pipelines.

Data Scientist 156

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ODSC - Open Data Science

FEBRUARY 24, 2023

Great Expectations provides support for different data backends such as flat file formats, SQL databases, Pandas dataframes and Sparks, and comes with built-in notification and data documentation functionality. VisiData works with CSV files, Excel spreadsheets, SQL databases, and many other data sources.

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