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.

SQL 130

SQL AWS Analytics Analytics 130

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. Amazon Redshift: Amazon Redshift is a cloud-based data warehousing service provided by Amazon Web Services (AWS).

Data Engineering 195

Data Engineering Data Engineering Data Engineer Data Engineering 195

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.

Data Pipeline 85

Data Pipeline ETL SQL Database 85

AWS Machine Learning Blog

SEPTEMBER 18, 2024

In addition to its groundbreaking AI innovations, Zeta Global has harnessed Amazon Elastic Container Service (Amazon ECS) with AWS Fargate to deploy a multitude of smaller models efficiently. It simplifies feature access for model training and inference, significantly reducing the time and complexity involved in managing data pipelines.

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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.

SQL 135

SQL AWS AI AI 135

Flipboard

MAY 21, 2025

This post highlights how the UBC CIC uses Amazon Web Services (AWS) to accelerate generative AI development, sharing lessons learned, tools used, and actionable insights you can apply to your projects. Security in generative AI prototyping UBC CIC observes the shared responsibility model through the Amazon Bedrock Data protection features.

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AWS AI AI Database 81

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

AWS Machine Learning Blog

AUGUST 12, 2024

Furthermore, the democratization of AI and ML through AWS and AWS Partner solutions is accelerating its adoption across all industries. For example, a health-tech company may be looking to improve patient care by predicting the probability that an elderly patient may become hospitalized by analyzing both clinical and non-clinical data.

ML 127

ML ML AWS AI 127

AWS Machine Learning Blog

MARCH 8, 2023

In order to train a model using data stored outside of the three supported storage services, the data first needs to be ingested into one of these services (typically Amazon S3). This requires building a data pipeline (using tools such as Amazon SageMaker Data Wrangler ) to move data into Amazon S3.

ML 135

ML ML AWS Python 135

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.

Data Pipeline 52

Data Pipeline Python Data Engineering Data Engineer 52

Pickl AI

MARCH 19, 2025

Summary: Data engineering tools streamline data collection, storage, and processing. Tools like Python, SQL, Apache Spark, and Snowflake help engineers automate workflows and improve efficiency. Learning these tools is crucial for building scalable data pipelines.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

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.

ETL 126

ETL Hadoop Data Warehouse Data Pipeline 126

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. If you’re familiar with SageMaker and writing Spark code, option B could be your choice.

ML 123

ML ML AWS Data Warehouse 123

Flipboard

NOVEMBER 24, 2023

This use case highlights how large language models (LLMs) are able to become a translator between human languages (English, Spanish, Arabic, and more) and machine interpretable languages (Python, Java, Scala, SQL, and so on) along with sophisticated internal reasoning.

Database 158

Database AWS ETL SQL 158

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.

Data Pipeline 52

Data Pipeline Database SQL Data Engineering 52

AWS Machine Learning Blog

SEPTEMBER 24, 2024

Working with the AWS Generative AI Innovation Center , DoorDash built a solution to provide Dashers with a low-latency self-service voice experience to answer frequently asked questions, reducing the need for live agent assistance, in just 2 months. “We You can deploy the solution in your own AWS account and try the example solution.

AWS 104

AWS AI AI Analytics 104

Data Science Dojo

JULY 3, 2024

Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud. Data Processing and Analysis : Techniques for data cleaning, manipulation, and analysis using libraries such as Pandas and Numpy in Python.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

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.

Data Science 111

Data Science Deep Learning Deep Learning Natural Language Processing 111

O'Reilly Media

SEPTEMBER 15, 2021

Cloud certifications, specifically in AWS and Microsoft Azure, were most strongly associated with salary increases. As we’ll see later, cloud certifications (specifically in AWS and Microsoft Azure) were the most popular and appeared to have the largest effect on salaries. Salaries were lower regardless of education or job title.

AI 145

AI AI Azure AWS 145

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

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.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

Women in Big Data

NOVEMBER 27, 2024

How to Choose a Data Warehouse for Your Big Data Choosing a data warehouse for big data storage necessitates a thorough assessment of your unique requirements. Begin by determining your data volume, variety, and the performance expectations for querying and reporting.

Data Warehouse 59

Data Warehouse Big Data Big Data Azure 59

phData

APRIL 29, 2024

SQL Server – The SQL Server connector, another widely-used database-type connector, provides similar functionality but is tailored for Microsoft’s SQL Server. The phData team achieved a major milestone by successfully setting up a secure end-to-end data pipeline for a substantial healthcare enterprise.

SQL 52

SQL Data Warehouse Azure Cloud Data 52

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 Engineer Data Engineering Data Engineering 52

phData

APRIL 28, 2025

Intuitive Workflow Design Workflows should be easy to follow and visually organized, much like clean, well-structured SQL or Python code. WHERE d.name = 'Sales'; Matillion is designed as a no/low-code ELT tool, so lets leave the SQL deep dive for another time and focus on making workflows as clean and intuitive as possible!

AI 52

AI AI SQL ETL 52

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. They are crucial in ensuring data is readily available for analysis and reporting.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Pickl AI

APRIL 21, 2025

Key Skills for Data Science: A data scientist typically needs a blend of skills: Mathematics and Statistics: To understand the theoretical underpinnings of models. Programming: Often in languages like Python or R, using libraries for data manipulation, analysis, and machine learning.

Big Data 52

Big Data Big Data Data Science Machine Learning 52

phData

JULY 2, 2024

Snowpark, offered by the Snowflake AI Data Cloud , consists of libraries and runtimes that enable secure deployment and processing of non-SQL code, such as Python, Java, and Scala. Developers can seamlessly build data pipelines, ML models, and data applications with User-Defined Functions and Stored Procedures.

Python 52

Python SQL Data Pipeline ML 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. Scalability: Designed to handle large volumes of data efficiently.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

phData

DECEMBER 4, 2023

If you answer “yes” to any of these questions, you will need cloud storage, such as Amazon AWS’s S3, Azure Data Lake Storage or GCP’s Google Storage. Copy Into When loading data into Snowflake, the very first and most important rule to follow is: do not load data with SQL inserts!

Clustering 98

Clustering Database SQL Data Pipeline 98

The MLOps Blog

JUNE 27, 2023

For example, if you use AWS, you may prefer Amazon SageMaker as an MLOps platform that integrates with other AWS services. SageMaker Studio offers built-in algorithms, automated model tuning, and seamless integration with AWS services, making it a powerful platform for developing and deploying machine learning solutions at scale.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

Pickl AI

APRIL 9, 2025

Integration : Can it connect with existing systems like AWS, Azure, or Google Cloud? It supports complex data transformations and offers advanced features like data quality management and metadata management. PowerCenter is particularly favored by large organizations with extensive data integration needs.

ETL 52

ETL Data Warehouse AWS Business Intelligence 52

IBM Journey to AI blog

JULY 17, 2023

.” Das Kamhout, VP and Senior Principal Engineer of the Cloud and Enterprise Solutions Group at Intel Watsonx.data supports our customers’ increasing needs around hybrid cloud deployments and is available on premises and across multiple cloud providers, including IBM Cloud and Amazon Web Services (AWS).

AI 74

AI AI Machine Learning Machine Learning 74

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

phData

FEBRUARY 14, 2023

Source data formats can only be Parquer, JSON, or Delimited Text (CSV, TSV, etc.). Streamsets Data Collector StreamSets Data Collector Engine is an easy-to-use data pipeline engine for streaming, CDC, and batch ingestion from any source to any destination.

Data Warehouse 59

Data Warehouse Azure AWS Database 59

phData

MAY 25, 2023

The June 2021 release of Power BI Desktop introduced Custom SQL queries to Snowflake in DirectQuery mode. However, Snowflake runs better on Azure than it does on AWS – so even though it’s not the ideal situation, Microsoft still sees Azure consumption when organizations host Snowflake on Azure.

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Power BI Analytics Analytics Azure 84

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.

ML 52

ML ML Data Lakes Machine Learning 52