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.

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ODSC - Open Data Science

APRIL 25, 2025

The two most prominent implementations are dbt Model Contracts , and the Open Data Contract Standard (ODCS, not to be confused withODSC. Sample dbt ModelContract Essentially, both solutions are JSON schema (widely used in APIs and event models), and thankfully, expressed in YAML for the ease of human typing.

Data Engineering 52

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IBM Journey to AI blog

MAY 9, 2023

We are also building models trained on different types of business data, including code, time-series data, tabular data, geospatial data and IT events data. With watsonx.data , businesses can quickly connect to data, get trusted insights and reduce data warehouse costs.

AI 110

AI AI Data Warehouse Machine Learning 110

Towards AI

FEBRUARY 13, 2023

Upcoming Community Events The Learn […] The steps involved include obtaining a list of texts to search, embedding the archive of questions, converting the embeddings into indexes, and others. Enjoy these papers and news summaries? Get a daily recap in your inbox! The Learn AI Together Community section!

AI 98

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Precisely

JUNE 24, 2024

They are working through organizational design challenges while also establishing foundational data management capabilities like metadata management and data governance that will allow them to offer trusted data to the business in a timely and efficient manner for analytics and AI.”

Analytics 94

Analytics Analytics Data Governance Data Lakes 94

Alation

FEBRUARY 13, 2020

It is about hurricanes and big events like the California wildfires, but it is also about complex things like satellite launches, for example, or big building projects. A lot of people in our audience are looking at implementing data lakes or are in the middle of big data lake initiatives.

Data Lakes 52

Data Lakes Analytics Analytics Data Engineering 52

Alation

DECEMBER 14, 2021

How do you provide access and connect the right people to the right data? AWS has created a way to manage policies and access, but this is only for data lake formation. What about other data sources? Redshift , AWS’ data warehouse that powers data exchange, provides 3x performance (3TB, 30 Tb, 100Tb dataset).

AWS 52

AWS Data Lakes Data Warehouse Machine Learning 52

ODSC - Open Data Science

JANUARY 30, 2024

Building and maintaining data pipelines Data integration is the process of combining data from multiple sources into a single, consistent view. This involves extracting data from various sources, transforming it into a usable format, and loading it into data warehouses or other storage systems.

Data Engineering 52

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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

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.

AI 88

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IBM Journey to AI blog

SEPTEMBER 19, 2023

Diagnostic analytics: Diagnostic analytics helps pinpoint the reason an event occurred. An electrical engineer can use prescriptive analytics to digitally design and test out various electrical systems to see expected energy output and predict the eventual lifespan of the system’s components.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

ODSC - Open Data Science

FEBRUARY 15, 2024

Data Pipeline Architecture — Stop Building Monoliths Elliott Cordo | Founder, Architect, Builder | Datafutures Although common, data monoliths present several challenges, especially for larger teams and organizations that allow for federated data product development. Interested in attending an ODSC event?

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

phData

JUNE 11, 2025

Whether you’re running small-scale analytics or managing enterprise-level data warehouses, these tips will help drive performance and meaningful business outcomes for your organization. Storage Costs Our first tip involves taking a closer look at managing how your data is stored, organized, and accessed.

Clustering 59

Clustering SQL Database Data Lakes 59

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.

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Pickl AI

JULY 30, 2024

Flow-Based Programming : NiFi employs a flow-based programming model, allowing users to create complex data flows using simple drag-and-drop operations. This visual representation simplifies the design and management of data pipelines. Guaranteed Delivery : NiFi ensures that data delivered reliably, even in the event of failures.

ETL 52

ETL Data Lakes Big Data Big Data 52

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. It is commonly used in data warehouses for business analytics and reporting.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 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 Engineering Data Engineer Data Engineering 52

Alation

MARCH 1, 2022

Lineage helps them identify the source of bad data to fix the problem fast. Manual lineage will give ARC a fuller picture of how data was created between AWS S3 data lake, Snowflake cloud data warehouse and Tableau (and how it can be fixed). Time is money,” said Leonard Kwok, Senior Data Analyst, ARC.

Data Warehouse 52

Data Warehouse Data Lakes Cloud Data Database 52

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

Pickl AI

FEBRUARY 23, 2025

Statistics : A survey by Databricks revealed that 80% of Spark users reported improved performance in their data processing tasks compared to traditional systems. Google Cloud BigQuery Google Cloud BigQuery is a fully-managed enterprise data warehouse that enables super-fast SQL queries using the processing power of Googles infrastructure.

Big Data 52

Big Data Big Data Apache Hadoop Apache Kafka 52

Pickl AI

FEBRUARY 21, 2023

Why is Data Mining Important? Data mining is often used to build predictive models that can be used to forecast future events. Moreover, data mining techniques can also identify potential risks and vulnerabilities in a business. The gathering of data requires assessment and research from various sources.

Data Mining 52

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Precisely

JANUARY 25, 2024

There are three potential approaches to mainframe modernization: Data Replication creates a duplicate copy of mainframe data in a cloud data warehouse or data lake, enabling high-performance analytics virtually in real time, without negatively impacting mainframe performance. Best Practice 5.

Data Governance 69

Data Governance Database Cloud Data Data Lakes 69

Pickl AI

AUGUST 9, 2024

Data Warehousing Solutions Tools like Amazon Redshift, Google BigQuery, and Snowflake enable organisations to store and analyse large volumes of data efficiently. Students should learn about the architecture of data warehouses and how they differ from traditional databases.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

ODSC - Open Data Science

JULY 11, 2023

Uninterruptible Power Supply (UPS): Provides backup power in the event of a power outage, to keep the equipment running long enough to perform an orderly shutdown. Cooling systems: Data centers generate a lot of heat, so they need cooling systems to keep the temperature at a safe level. Not a cloud computer?

Data Lakes 40

Data Lakes Cloud Computing AI AI 40

Pickl AI

SEPTEMBER 18, 2024

It utilises Amazon Web Services (AWS) as its main data lake, processing over 550 billion events daily—equivalent to approximately 1.3 petabytes of data. The architecture is divided into two main categories: data at rest and data in motion.

Big Data 52

Big Data Big Data Apache Kafka Big Data Analytics 52

phData

MAY 13, 2024

Built for integration, scalability, governance, and industry-leading security, Snowflake optimizes how you can leverage your organization’s data, providing the following benefits: Built to Be a Source of Truth Snowflake is built to simplify data integration wherever it lives and whatever form it takes.

Database 52

Database Analytics Analytics Machine Learning 52

Alation

APRIL 19, 2022

Data modernization is the process of transferring data to modern cloud-based databases from outdated or siloed legacy databases, including structured and unstructured data. In that sense, data modernization is synonymous with cloud migration. Efficient Data Processing. Enhanced Accessibility.

Data Governance 52

Data Governance Cloud Data Database Data Silos 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 Engineer Data Engineering 52

Flipboard

DECEMBER 6, 2023

The Q4 Platform facilitates interactions across the capital markets through IR website products, virtual events solutions, engagement analytics, investor relations Customer Relationship Management (CRM), shareholder and market analysis, surveillance, and ESG tools. Use case overview Q4 Inc.,

SQL 168

SQL Database AWS Machine Learning 168

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.

SQL 52

SQL Database Data Quality Data Warehouse 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 Data Quality SQL 59

IBM Journey to AI blog

OCTOBER 16, 2023

ML classification algorithms are also used to label events as fraud, classify phishing attacks and more. Scale AI workloads anywhere, for all your data, with watsonx.data Enable responsible, transparent and explainable data and AI workflows with watsonx.governance The post 10 everyday machine learning use cases appeared first on IBM Blog.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Alation

APRIL 14, 2022

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. Similar to a data warehouse schema, this prep tool automates the development of the recipe to match. Scheduling. Target Matching.

Data Governance 59

Data Governance ML ML Cloud Data 59

Alation

FEBRUARY 13, 2020

Overcoming the challenges of data catalog adoption takes more than patience and persistence. You’ll need a plan that recognizes adoption as a process that unfolds over time—not an event that happens at a point in time. People – Which data stakeholders will you bring on board at what times?

Data Governance 52

Data Governance Data Analysis Data Analysis Data Preparation 52

Alation

FEBRUARY 13, 2020

And celebrating the ability for Machine Learning Data Catalogs to support data culture in every enterprise. Join us at one of our upcoming events and share your story of human-machine collaboration to create data culture. Follow us at #MLDC as we tour the world. – John F.

Machine Learning 52

Machine Learning Machine Learning Big Data Big Data 52

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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Data Science Blog

FEBRUARY 7, 2023

Schon damals in Ansätzen, aber spätestens heute gilt es zu recht als Best Practise, die Datenanbindung an ein Data Warehouse zu machen und in diesem die Daten für die Reports aufzubereiten. Ein Data Warehouse ist eine oder eine Menge von Datenbanken. Was gerade zum Trend wird, ist der Aufbau eines Data Lakehouses.

Business Intelligence 286

Business Intelligence Business Intelligence Data Warehouse Data Lakes 286