Adrian Bridgwater for Forbes

DECEMBER 1, 2022

What businesses need from cloud computing is the power to work on their data without having to transport it around between different clouds, different databases and different repositories, different integrations to third-party applications, different data pipelines and different compute engines.

Cloud Computing 130

Cloud Computing Data Pipeline AWS Database 130

Women in Big Data

OCTOBER 9, 2024

With the explosive growth of big data over the past decade and the daily surge in data volumes, it’s essential to have a resilient system to manage the vast influx of information without failures. The success of any data initiative hinges on the robustness and flexibility of its big data pipeline.

Big Data 59

Big Data Big Data Apache Kafka Data Pipeline 59

Pickl AI

DECEMBER 26, 2024

Summary: “Data Science in a Cloud World” highlights how cloud computing transforms Data Science by providing scalable, cost-effective solutions for big data, Machine Learning, and real-time analytics. This accessibility democratises Data Science, making it available to businesses of all sizes.

Data Science 52

Data Science Cloud Computing Machine Learning Machine Learning 52

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.

AWS 119

AWS Machine Learning Machine Learning ML 119

AWS Machine Learning Blog

FEBRUARY 1, 2024

Consider the following picture, which is an AWS view of the a16z emerging application stack for large language models (LLMs). This pipeline could be a batch pipeline if you prepare contextual data in advance, or a low-latency pipeline if you’re incorporating new contextual data on the fly.

AWS 135

AWS AI AI Database 135

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

AWS Machine Learning Blog

OCTOBER 23, 2024

SnapLogic uses Amazon Bedrock to build its platform, capitalizing on the proximity to data already stored in Amazon Web Services (AWS). Control plane and data plane implementation SnapLogic’s Agent Creator platform follows a decoupled architecture, separating the control plane and data plane for enhanced security and scalability.

AI 81

AI AI AWS Database 81

Flipboard

NOVEMBER 24, 2023

In this post, we will talk about how BMW Group, in collaboration with AWS Professional Services, built its Jupyter Managed (JuMa) service to address these challenges. For example, teams using these platforms missed an easy migration of their AI/ML prototypes to the industrialization of the solution running on AWS.

ML 152

ML ML AWS AI 152

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

AWS Machine Learning Blog

FEBRUARY 13, 2024

SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM). Their task is to construct and oversee efficient data pipelines.

AWS 129

AWS ML ML Machine Learning 129

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 118

ML ML AWS AI 118

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 131

ML ML AWS Python 131

Flipboard

NOVEMBER 24, 2023

In this post, we show you how SnapLogic , an AWS customer, used Amazon Bedrock to power their SnapGPT product through automated creation of these complex DSL artifacts from human language. SnapLogic background SnapLogic is an AWS customer on a mission to bring enterprise automation to the world.

Database 157

Database AWS ETL SQL 157

AWS Machine Learning Blog

AUGUST 8, 2024

As one of the largest AWS customers, Twilio engages with data, artificial intelligence (AI), and machine learning (ML) services to run their daily workloads. Data is the foundational layer for all generative AI and ML applications. Access to Amazon Bedrock FMs isn’t granted by default.

SQL 122

SQL Data Lakes Data Analyst AWS 122

ODSC - Open Data Science

FEBRUARY 2, 2023

Big Data As datasets become larger and more complex, knowing how to work with them will be key. Big data isn’t an abstract concept anymore, as so much data comes from social media, healthcare data, and customer records, so knowing how to parse all of that is needed.

Data Science 98

Data Science Data Scientist Computer Science Computer Science 98

AWS Machine Learning Blog

SEPTEMBER 11, 2024

Examples of other PBAs now available include AWS Inferentia and AWS Trainium , Google TPU, and Graphcore IPU. Around this time, industry observers reported NVIDIA’s strategy pivoting from its traditional gaming and graphics focus to moving into scientific computing and data analytics.

AWS 101

AWS ML ML Clustering 101

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

Precisely

JANUARY 19, 2023

Organizations that can master the challenges of data integration, data quality, and context will be well positioned to identify opportunities and threats quickly, and then to take decisive action to gain competitive advantage. Mainframes have long been valued for those very same attributes.

AWS 52

AWS Cloud Computing Data Pipeline Big Data 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.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

Pickl AI

OCTOBER 10, 2024

Enhanced Data Quality : These tools ensure data consistency and accuracy, eliminating errors often occurring during manual transformation. Scalability : Whether handling small datasets or processing big data, transformation tools can easily scale to accommodate growing data volumes.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 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 Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Pickl AI

NOVEMBER 4, 2024

Introduction Data Engineering is the backbone of the data-driven world, transforming raw data into actionable insights. As organisations increasingly rely on data to drive decision-making, understanding the fundamentals of Data Engineering becomes essential. What is Data Engineering? million by 2028.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Hacker News

JUNE 29, 2023

Harnessing that customer data and getting it to the marketing and analytics tools that require it has always been a challenge….until Freshpaint is a Customer Data Platform that powers the entire customer data pipeline and integrates all your tools.

Analytics 94

Analytics Analytics Data Pipeline Big Data 94

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

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. Read More: Advanced SQL Tips and Tricks for Data Analysts.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

DataRobot Blog

OCTOBER 3, 2017

With this integration, customers can now harness the full power of Azure’s Big Data offerings in a self-service manner to gain immediate value.”. This highlights the two companies’ shared vision on self-service data discovery with an emphasis on collaboration and data governance.

Data Lakes 52

Data Lakes Azure Data Pipeline Hadoop 52

IBM Journey to AI blog

AUGUST 6, 2024

Today, all leading CSPs, including Amazon Web Services (AWS Lambda), Microsoft Azure (Azure Functions) and IBM (IBM Cloud Code Engine) offer serverless platforms. Big data analytics Serverless dramatically reduces the cost and complexity of writing and deploying code for big data applications.

Cloud Computing 45

Cloud Computing Internet of Things Big Data Big Data 45

Precisely

MAY 13, 2024

A true enterprise-grade integration solution calls for source and target connectors that can accommodate: VSAM files COBOL copybooks open standards like JSON modern platforms like Amazon Web Services ( AWS ), Confluent , Databricks , or Snowflake Questions to ask each vendor: Which enterprise data sources and targets do you support?

Data Governance 52

Data Governance Data Pipeline Cloud Data Data Quality 52

Mlearning.ai

JULY 11, 2023

This includes important stages such as feature engineering, model development, data pipeline construction, and data deployment. For example, when it comes to deploying projects on cloud platforms, different companies may utilize different providers like AWS, GCP, or Azure.

Data Science 40

Data Science Exploratory Data Analysis Azure Power BI 40

IBM Journey to AI blog

SEPTEMBER 4, 2024

Kafka helps simplify the communication between customers and businesses, using its data pipeline to accurately record events and keep records of orders and cancellations—alerting all relevant parties in real-time. Developers using Apache can speed app development with support for whatever requirements their organization has.

Apache Kafka 58

Apache Kafka Internet of Things Data Pipeline Clustering 58

ODSC - Open Data Science

JANUARY 7, 2025

Environments Data science environments encompass the tools and platforms where professionals perform their work. From development environments like Jupyter Notebooks to robust cloud-hosted solutions such as AWS SageMaker, proficiency in these systems is critical.

Data Scientist 40

Data Scientist Data Science Machine Learning Machine Learning 40

DagsHub

OCTOBER 23, 2024

With proper unstructured data management, you can write validation checks to detect multiple entries of the same data. Continuous learning: In a properly managed unstructured data pipeline, you can use new entries to train a production ML model, keeping the model up-to-date.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Flipboard

MARCH 21, 2025

Through this unified query capability, you can create comprehensive insights into customer transaction patterns and purchase behavior for active products without the traditional barriers of data silos or the need to copy data between systems. Environments are the actual data infrastructure behind a project.

SQL 135

SQL Data Analyst Data Warehouse AWS 135

Flipboard

NOVEMBER 22, 2024

Troubleshooting these production issues requires extensive analysis of logs and metrics, often leading to extended downtimes and delayed insights from critical data pipelines. Today, we are excited to announce the preview of generative AI troubleshooting for Spark in AWS Glue. Your jobs must run on AWS Glue version 4.0.

AWS 143

AWS AI AI Data Engineering 143

The MLOps Blog

AUGUST 11, 2023

Internally within Netflix’s engineering team, Meson was built to manage, orchestrate, schedule, and execute workflows within ML/Data pipelines. Meson managed the lifecycle of ML pipelines, providing functionality such as recommendations and content analysis, and leveraged the Single Leader Architecture.

ML 52

ML ML Machine Learning Machine Learning 52

AWS Machine Learning Blog

OCTOBER 24, 2024

Data pipelines must seamlessly integrate new data at scale. Diverse data amplifies the need for customizable cleaning and transformation logic to handle the quirks of different sources. To facilitate effective retrieval from external data, a common practice is to first clean up and sanitize the documents.

AWS 117

AWS Data Pipeline Database Big Data 117

AWS Machine Learning Blog

NOVEMBER 12, 2024

This post describes how Agmatix uses Amazon Bedrock and AWS fully featured services to enhance the research process and development of higher-yielding seeds and sustainable molecules for global agriculture. AWS generative AI services provide a solution In addition to other AWS services, Agmatix uses Amazon Bedrock to solve these challenges.

AWS 112

AWS AI AI Data Lakes 112