Flipboard

JANUARY 24, 2025

RAG data store The Retrieval Augmented Generation (RAG) data store delivers up-to-date, precise, and access-controlled knowledge from various data sources such as data warehouses, databases, and other software as a service (SaaS) applications through data connectors.

AWS 158

AWS AI AI Data Warehouse 158

Dataconomy

JUNE 1, 2023

Unified data storage : Fabric’s centralized data lake, Microsoft OneLake, eliminates data silos and provides a unified storage system, simplifying data access and retrieval. Flexible compute capacity One of the key advantages of Microsoft Fabric is its ability to optimize compute capacity across different workloads.

Power BI 194

Power BI Data Lakes Azure Data Silos 194

AWS Machine Learning Blog

JANUARY 6, 2023

The rules in this engine were predefined and written in SQL, which aside from posing a challenge to manage, also struggled to cope with the proliferation of data from TR’s various integrated data source. TR customer data is changing at a faster rate than the business rules can evolve to reflect changing customer needs.

AWS 95

AWS Data Warehouse ML ML 95

IBM Journey to AI blog

NOVEMBER 3, 2023

Apache Kafka and Apache Flink working together Anyone who is familiar with the stream processing ecosystem is familiar with Apache Kafka: the de-facto enterprise standard for open-source event streaming. Apache Kafka streams get data to where it needs to go, but these capabilities are not maximized when Apache Kafka is deployed in isolation.

Apache Kafka 79

Apache Kafka Data Warehouse Data Pipeline Big Data 79

IBM Journey to AI blog

SEPTEMBER 11, 2023

Codd published his famous paper “ A Relational Model of Data for Large Shared Data Banks.” Boyce to create Structured Query Language (SQL). Developers can leverage features like REST APIs, JSON support and enhanced SQL compatibility to easily build cloud-native applications. Chamberlin and Raymond F.

Database 101

Database SQL Data Warehouse Machine Learning 101

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

Tableau

MARCH 9, 2021

We all missed meeting in-person this year—that real-life connection is hard to replace for relationship-building, fast decision making, and having a little social time together—but heard great feedback across all three Theaters about this year’s digital event. Despite all the headwinds, we are persisting and growing together.

Tableau 98

Tableau AWS Data Warehouse SQL 98

Smart Data Collective

JULY 20, 2021

. #10 Panoply: In the world of CRM technology, Panoply is a data warehouse build that automates data collection, query optimization and storage management. This tool will help you to sync and store data from multiple sources quickly. With this tool, data transfer is faster and dynamic.

Big Data 142

Big Data Big Data ETL Analytics 142

Hacker News

AUGUST 27, 2024

Policy Zones has been built into different Meta systems, including: Function-based systems that load, process, and propagate data through stacks of function calls in different programming languages. Batch-processing systems that process data rows in batch (mainly via SQL ). When data flows across different systems (e.g.,

Data Warehouse 166

Data Warehouse Database SQL Machine Learning 166

Smart Data Collective

FEBRUARY 20, 2022

On the one hand, the use of agents allows you to actively monitor and respond to events. A promising trend is the refinement of these systems’ UBA functionality through machine learning methods that help analyze chains of events, establish baseline activity patterns, and find deviations from normal user behavior.

Database 130

Database Machine Learning Machine Learning Data Warehouse 130

Dataconomy

APRIL 27, 2023

They are also designed to handle concurrent access by multiple users and applications, while ensuring data integrity and transactional consistency. Examples of OLTP databases include Oracle Database, Microsoft SQL Server, and MySQL. An OLAP database may also be organized as a data warehouse.

Database 159

Database Data Scientist Data Mining Data Mining 159

Mlearning.ai

FEBRUARY 16, 2023

The ultimate need for vast storage spaces manifests in data warehouses: specialized systems that aggregate data coming from numerous sources for centralized management and consistency. In this article, you’ll discover what a Snowflake data warehouse is, its pros and cons, and how to employ it efficiently.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

IBM Journey to AI blog

SEPTEMBER 19, 2023

And you should have experience working with big data platforms such as Hadoop or Apache Spark. Additionally, data science requires experience in SQL database coding and an ability to work with unstructured data of various types, such as video, audio, pictures and text.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

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 Engineering Data Engineering Data Engineer 52

phData

SEPTEMBER 5, 2023

Google Analytics 4 (GA4) is a powerful tool for collecting and analyzing website and app data that many businesses rely heavily on to make informed business decisions. However, there might be instances where you need to migrate the raw event data from GA4 to Snowflake for more in-depth analysis and business intelligence purposes.

Azure 52

Azure Analytics Analytics Data Pipeline 52

AWS Machine Learning Blog

JULY 17, 2023

Amazon Redshift is a fully managed, fast, secure, and scalable cloud data warehouse. Organizations often want to use SageMaker Studio to get predictions from data stored in a data warehouse such as Amazon Redshift. This should return the records successfully for further data processing and analysis.

Clustering 94

Clustering AWS ML ML 94

phData

FEBRUARY 14, 2023

Data integration is essentially the Extract and Load portion of the Extract, Load, and Transform (ELT) process. Data ingestion involves connecting your data sources, including databases, flat files, streaming data, etc, to your data warehouse. Snowflake provides native ways for data ingestion.

Data Warehouse 59

Data Warehouse Azure AWS Database 59

phData

OCTOBER 20, 2023

Some of the databases supported by Fivetran are: Snowflake Data Cloud (BETA) MySQL PostgreSQL SAP ERP SQL Server Oracle In this blog, we will review how to pull Data from on-premise Systems using Fivetran to a specific target or destination. The most common example of such databases is where events are tracked.

Database 52

Database SQL ETL Data Warehouse 52

phData

FEBRUARY 5, 2024

The tool converts the templated configuration into a set of SQL commands that are executed against the target Snowflake environment. Replicate can interact with a wide variety of databases, data warehouses, and data lakes (on-premise or based in the cloud). It is also a helpful tool for learning a new SQL dialect.

SQL 52

SQL Database Data Quality Data Warehouse 52

Tableau

MARCH 9, 2021

We all missed meeting in-person this year—that real-life connection is hard to replace for relationship-building, fast decision making, and having a little social time together—but heard great feedback across all three Theaters about this year’s digital event. Despite all the headwinds, we are persisting and growing together.

Tableau 52

Tableau AWS Data Warehouse SQL 52

phData

OCTOBER 17, 2024

The DAGs can then be scheduled to run at specific intervals or triggered when an event occurs. dbt offers a SQL-first transformation workflow that lets teams build data transformation pipelines while following software engineering best practices like CI/CD, modularity, and documentation.

AI 52

AI AI SQL Data Quality 52

Pickl AI

AUGUST 11, 2024

These tables are called “factless fact tables” or “junction tables” They are used for modelling many-to-many relationships or for capturing timestamps of events. Dealing with Sparse Data In some cases, fact tables may contain a large number of null values due to missing data.

Data Quality 52

Data Quality Data Warehouse Data Governance Analytics 52

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

The MLOps Blog

MARCH 15, 2023

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 Ingestion : Involves raw data collection from origin and storage using architectures such as batch, streaming or event-driven.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

Pickl AI

AUGUST 9, 2024

Understanding the differences between SQL and NoSQL databases is crucial for students. Data Warehousing Solutions Tools like Amazon Redshift, Google BigQuery, and Snowflake enable organisations to store and analyse large volumes of data efficiently. Once data is collected, it needs to be stored efficiently.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

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. You can also get data science training on-demand wherever you are with our Ai+ Training platform.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

phData

MARCH 29, 2024

This evolved into the phData Toolkit , a collection of high-quality data applications to help you migrate, validate, optimize, and secure your data. Operational Risks: Uncover operational risks such as data loss or failures in the event of an unforeseen outage or disaster.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Dataconomy

MARCH 24, 2023

They may also be involved in data modeling and database design. BI developer:  A BI developer is responsible for designing and implementing BI solutions, including data warehouses, ETL processes, and reports. They may also be involved in data integration and data quality assurance.

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

Dataconomy

MARCH 24, 2023

They may also be involved in data modeling and database design. BI developer:  A BI developer is responsible for designing and implementing BI solutions, including data warehouses, ETL processes, and reports. They may also be involved in data integration and data quality assurance.

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

Alation

OCTOBER 27, 2022

Few actors in the modern data stack have inspired the enthusiasm and fervent support as dbt. This data transformation tool enables data analysts and engineers to transform, test and document data in the cloud data warehouse. But what does this mean from a practitioner perspective?

Data Analyst 52

Data Analyst Data Scientist Analytics Analytics 52

AWS Machine Learning Blog

FEBRUARY 28, 2023

Query the data using Athena By running Athena SQL queries directly on Amazon HealthLake, we are able to select only those fields that are not personally identifying; for example, not selecting name and patient ID, and reducing birthdate to birth year. In this post, we used Amazon S3 as the input data source for SageMaker Canvas.

ML 99

ML ML AWS Machine Learning 99

Snorkel AI

JUNE 27, 2023

First, you generate predictions and you store them in a data warehouse. So we write a SQL definition. And then during prediction, we can use stream SQL to compute these SQL features. We should be able to continually train the model on fresh data. So we need to access fresh data.

AI 52

AI AI Data Warehouse Machine Learning 52

phData

JUNE 3, 2024

Fail Safe doesn’t allow you to query the data within it; it is simply there to protect your data from catastrophic failure. The Snowflake team can use Fail Safe to restore your data in the event of an extreme operational failure, giving you even more peace of mind. Snowflake has you covered with Cortex.

Machine Learning 52

Machine Learning Machine Learning Database Cloud Data 52

Pickl AI

SEPTEMBER 5, 2024

However, a master’s degree or specialised Data Science or Machine Learning courses can give you a competitive edge, offering advanced knowledge and practical experience. Essential Technical Skills Technical proficiency is at the heart of an Azure Data Scientist’s role.

Azure 52

Azure Data Scientist Data Science Machine Learning 52

phData

OCTOBER 25, 2024

Data Quality Monitoring implements quality checks in operational data processes to ensure that the data meets pre-defined standards and business rules. This results in poor credibility and data consistency after some time, leading businesses to mistrust the data pipelines and processes.

Data Quality 52

Data Quality Data Pipeline Data Governance Database 52

Snorkel AI

JUNE 27, 2023

First, you generate predictions and you store them in a data warehouse. So we write a SQL definition. And then during prediction, we can use stream SQL to compute these SQL features. We should be able to continually train the model on fresh data. So we need to access fresh data.

AI 52

AI AI Data Warehouse Machine Learning 52

Snorkel AI

JUNE 27, 2023

First, you generate predictions and you store them in a data warehouse. So we write a SQL definition. And then during prediction, we can use stream SQL to compute these SQL features. We should be able to continually train the model on fresh data. So we need to access fresh data.

AI 52

AI AI Data Warehouse Machine Learning 52