ODSC - Open Data Science

FEBRUARY 15, 2024

These systems represent data as knowledge graphs and implement graph traversal algorithms to help find content in massive datasets. These systems are not only useful for a wide range of industries, they are fun for data engineers to work on. Interested in attending an ODSC event? Learn more about our upcoming events here.

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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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ODSC - Open Data Science

OCTOBER 15, 2023

Cloudera Cloudera is a cloud-based platform that provides businesses with the tools they need to manage and analyze data. They offer a variety of services, including data warehousing, data lakes, and machine learning. The platform includes several features that make it easy to develop and test data pipelines.

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ODSC - Open Data Science

DECEMBER 19, 2023

Data Engineer Data engineers are responsible for the end-to-end process of collecting, storing, and processing data. They use their knowledge of data warehousing, data lakes, and big data technologies to build and maintain data pipelines. Interested in attending an ODSC event?

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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.

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ODSC - Open Data Science

JANUARY 18, 2024

Data engineers will also work with data scientists to design and implement data pipelines; ensuring steady flows and minimal issues for data teams. They’ll also work with software engineers to ensure that the data infrastructure is scalable and reliable. Interested in attending an ODSC event?

Data Engineer 52

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phData

SEPTEMBER 27, 2024

Methods that allow our customer data models to be as dynamic and flexible as the customers they represent. In this guide, we will explore concepts like transitional modeling for customer profiles, the power of event logs for customer behavior, persistent staging for raw customer data, real-time customer data capture, and much more.

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IBM Journey to AI blog

SEPTEMBER 19, 2023

By analyzing datasets, data scientists can better understand their potential use in an algorithm or machine learning model. The data science lifecycle Data science is iterative, meaning data scientists form hypotheses and experiment to see if a desired outcome can be achieved using available data.

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AWS Machine Learning Blog

DECEMBER 20, 2023

A novel approach to solve this complex security analytics scenario combines the ingestion and storage of security data using Amazon Security Lake and analyzing the security data with machine learning (ML) using Amazon SageMaker. Store new security logs in an S3 bucket and queue events in Amazon Simple Queue Service (Amazon SQS).

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IBM Journey to AI blog

OCTOBER 16, 2023

Curated foundation models, such as those created by IBM or Microsoft, help enterprises scale and accelerate the use and impact of the most advanced AI capabilities using trusted data. In addition to natural language, models are trained on various modalities, such as code, time-series, tabular, geospatial and IT events data.

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The MLOps Blog

JUNE 27, 2023

LakeFS LakeFS is an open-source platform that provides data lake versioning and management capabilities. It sits between the data lake and cloud object storage, allowing you to version and control changes to data lakes at scale. Flyte Flyte is a platform for orchestrating ML pipelines at scale.

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phData

AUGUST 5, 2024

Data storage flexibility is beneficial for customers who cannot (or choose not) to store their data lakes in Snowflake. Operational Risks: Identify operational risks, such as data loss or failures, in the event of an unforeseen outage or disaster.

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AWS Machine Learning Blog

FEBRUARY 13, 2024

Collaboration across teams – Shared features allow disparate teams like fraud, marketing, and sales to collaborate on building ML models using the same reliable data instead of creating siloed features. Audit trail for compliance – Administrators can monitor feature usage by all accounts centrally using CloudTrail event logs.

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phData

FEBRUARY 5, 2024

Other features include email notifications (to let you know if a job failed or is running long), job scheduling, orchestration to ensure your data gets to Snowflake when you want it, and of course, full automation of your complete data ingestion process.

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AWS Machine Learning Blog

APRIL 12, 2023

Traditional maintenance activities rely on a sizable workforce distributed across key locations along the BHS dispatched by operators in the event of an operational fault. With this service, industrial sensors, smart meters, and OPC UA servers can be connected to an AWS data lake with just a few clicks.

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Pickl AI

JULY 25, 2024

Must Read Blogs: Exploring the Power of Data Warehouse Functionality. Data Lakes Vs. Data Warehouse: Its significance and relevance in the data world. Exploring Differences: Database vs Data Warehouse. Explore More: Build Data Pipelines: Comprehensive Step-by-Step Guide.

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phData

FEBRUARY 14, 2023

What Are the Best Third-Party Data Ingestion Tools for Snowflake? Fivetran Fivetran is a tool dedicated to replicating applications, databases, events, and files into a high-performance data warehouse, such as Snowflake. Source data formats can only be Parquer, JSON, or Delimited Text (CSV, TSV, etc.).

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Pickl AI

SEPTEMBER 5, 2024

Storage Solutions: Secure and scalable storage options like Azure Blob Storage and Azure Data Lake Storage. Key features and benefits of Azure for Data Science include: Scalability: Easily scale resources up or down based on demand, ideal for handling large datasets and complex computations.

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Alation

APRIL 14, 2022

Data pipeline orchestration. Moving/integrating data in the cloud/data exploration and quality assessment. Supports the ability to interact with the actual data and perform analysis on it. This provides the facility a time or event for a job to run and offers useful post-run information. Scheduling.

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Mlearning.ai

FEBRUARY 16, 2023

Thus, the solution allows for scaling data workloads independently from one another and seamlessly handling data warehousing, data lakes , data sharing, and engineering. You can use Snowflake cloud computing to store raw data in structured or variant format, using various data models to meet the needs.

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Snorkel AI

MARCH 2, 2023

We have data pipelines and data preparation. So each of them may require some repositories from a data lake house/analytics hub kind of thing for sharing data, to a feature store, to a model hub, to the responsible AI (known sets of things that you need to guard against), to a model registry.

Machine Learning 52

Machine Learning Machine Learning Data Preparation AI 52

Snorkel AI

MARCH 2, 2023

We have data pipelines and data preparation. So each of them may require some repositories from a data lake house/analytics hub kind of thing for sharing data, to a feature store, to a model hub, to the responsible AI (known sets of things that you need to guard against), to a model registry.

Machine Learning 52

Machine Learning Machine Learning Data Preparation AI 52

DataRobot Blog

OCTOBER 3, 2017

Enterprise data architects, data engineers, and business leaders from around the globe gathered in New York last week for the 3-day Strata Data Conference , which featured new technologies, innovations, and many collaborative ideas. 2) When data becomes information, many (incremental) use cases surface.

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Alation

APRIL 19, 2022

In that sense, data modernization is synonymous with cloud migration. Modern data architectures, like cloud data warehouses and cloud data lakes , empower more people to leverage analytics for insights more efficiently. Access the resources your data applications need — no more, no less. Advanced Tooling.

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The MLOps Blog

MARCH 21, 2023

The service will consume the features in real time, generate predictions in near real-time , such as in an event processing pipeline, and write the outputs to a prediction queue. Solution Data lakes and warehouses are the two key components of any data pipeline. Data engineers are mostly in charge of it.

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