Dataconomy

SEPTEMBER 4, 2023

Diagnostic analytics: Diagnostic analytics goes a step further by analyzing historical data to determine why certain events occurred. By understanding the “why” behind past events, organizations can make informed decisions to prevent or replicate them. Ensure that data is clean, consistent, and up-to-date.

Analytics 203

Analytics Analytics Big Data Analytics Big Data Analytics 203

Pickl AI

FEBRUARY 23, 2025

Statistics : According to AWS reports, EMR reduces the time required for Big Data processing tasks by up to 90% compared to traditional methods. Microsoft Azure HDInsight Azure HDInsight is a fully-managed cloud service that makes it easy to process Big Data using popular open-source frameworks such as Hadoop, Spark, and Kafka.

Big Data 52

Big Data Big Data Apache Hadoop Apache Kafka 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. free trial.

Data Lakes 52

Data Lakes Azure Data Pipeline Hadoop 52

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).

AWS 134

AWS ML ML Algorithm 134

phData

APRIL 29, 2024

Recognizing these specific needs, Fivetran has developed a range of connectors, including dedicated applications, databases, files, and events, which can accommodate the diverse formats used by healthcare systems. Addressing these needs may pose challenges that lead to the implementation of custom solutions rather than a uniform approach.

SQL 52

SQL Data Warehouse Azure Cloud Data 52

phData

JUNE 11, 2025

Depending on the requirement, it is important to choose between transient and permanent tables, as well as data recovery needs and downtime considerations. Implement scripts or workflows to automatically tag new resources based on predefined criteria or events, reducing manual effort and ensuring timely and accurate cost attribution.

Clustering 59

Clustering SQL Database Data Lakes 59

ODSC - Open Data Science

JULY 22, 2023

On Wednesday, Henk Boelman, Senior Cloud Advocate at Microsoft, spoke about the current landscape of Microsoft Azure, as well as some interesting use cases and recent developments. Expo Hall ODSC events are more than just data science training and networking events. What’s next?

Apache Kafka 52

Apache Kafka Machine Learning Machine Learning Data Science 52

Snorkel AI

OCTOBER 9, 2024

Enterprise IT admins can configure access to features and data at an instance, workspace, or role level by leveraging a ccess control rules. Snorkel automatically provisions those users with locked-down feature & data access to a set of permissioned workspaces.

Azure 52

Azure AWS Data Lakes Clustering 52

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. Tips When Considering ADF: ADF will only write to Snowflake accounts that are based in Azure.

Data Warehouse 59

Data Warehouse Azure AWS Database 59

ODSC - Open Data Science

JANUARY 18, 2024

They’ll also work with software engineers to ensure that the data infrastructure is scalable and reliable. These professionals will work with their colleagues to ensure that data is accessible, with proper access. The reason this is an important skill is that ETL is a critical process for data warehousing and business intelligence.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Pickl AI

NOVEMBER 4, 2024

Role of Data Engineers in the Data Ecosystem Data Engineers play a crucial role in the data ecosystem by bridging the gap between raw data and actionable insights. They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

ODSC - Open Data Science

OCTOBER 3, 2024

AI and Data: Enhancing Development with GitHub Copilot How can GitHub Copilot be used in environments like Visual Studio Code, JetBrains IDEs, or Azure Data Studio to significantly reduce coding time? Industry, Opinion, Career Advice AI for Robotics and Autonomy with Francis X.

Data Science 40

Data Science Data Lakes Data Scientist AI 40

ODSC - Open Data Science

OCTOBER 20, 2023

At the AI Expo and Demo Hall as part of ODSC West in a few weeks, you’ll have the opportunity to meet one-on-one with representatives from industry-leading organizations like Microsoft Azure, Hewlett Packard, Iguazio, neo4j, Tangent Works, Qwak, Cloudera, and others. Interested in attending an ODSC event?

AI 52

AI AI Data Science Machine Learning 52

The MLOps Blog

JUNE 27, 2023

Microsoft Azure ML Platform The Azure Machine Learning platform provides a collaborative workspace that supports various programming languages and frameworks. LakeFS LakeFS is an open-source platform that provides data lake versioning and management capabilities.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

ODSC - Open Data Science

JULY 11, 2023

Co-location data centers: These are data centers that are owned and operated by third-party providers and are used to house the IT equipment of multiple organizations. Edge data centers: These are data centers that are located closer to the edge of the network, where data is generated and consumed, rather than in central locations.

Data Lakes 40

Data Lakes AI AI Cloud Computing 40

DagsHub

OCTOBER 23, 2024

To combine the collected data, you can integrate different data producers into a data lake as a repository. A central repository for unstructured data is beneficial for tasks like analytics and data virtualization. Data Cleaning The next step is to clean the data after ingesting it into the data lake.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

phData

JULY 10, 2023

It can be used to store data outside the database while retaining the ability to query its data. These files need to be in one of the Snowflake-supported cloud systems: Amazon S3, Google Cloud Storage, or Microsoft Azure Blob storage. What are Directory Tables in Snowflake?

Data Lakes 40

Data Lakes Azure Database AWS 40

Mlearning.ai

FEBRUARY 16, 2023

The platform enables quick, flexible, and convenient options for storing, processing, and analyzing data. The solution was built on top of Amazon Web Services and is now available on Google Cloud and Microsoft Azure. Each model carries its specific benefits and allows for reloading and reprocessing of data in the event of errors.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

Alation

APRIL 14, 2022

This two-part series will explore how data discovery, fragmented data governance , ongoing data drift, and the need for ML explainability can all be overcome with a data catalog for accurate data and metadata record keeping. The Cloud Data Migration Challenge. Automatic sampling to test transformation.

Data Governance 59

Data Governance ML ML Cloud Data 59

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.

Machine Learning 69

Machine Learning Machine Learning Data Scientist ML 69

Data Science Blog

FEBRUARY 7, 2023

Die Daten bereiten wir in sogenannte Event Logs, also Prozessprotokolle, auf und laden sie dann ein eines der vielen Process Mining Tools, egal in welches. Was gerade zum Trend wird, ist der Aufbau eines Data Lakehouses. Ein Lakehouse inkludiert auch clevere Art und Weise auch einen Data Lake.

Business Intelligence 286

Business Intelligence Business Intelligence Data Warehouse Data Lakes 286

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.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52