Dataconomy

SEPTEMBER 4, 2023

Key features of cloud analytics solutions include: Data models , Processing applications, and Analytics models. Data models help visualize and organize data, processing applications handle large datasets efficiently, and analytics models aid in understanding complex data sets, laying the foundation for business intelligence.

Analytics 203

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IBM Journey to AI blog

NOVEMBER 29, 2023

It automatically identifies vulnerable individual data points and introduces “noise” to obscure their specific information. Although adding noise slightly reduces output accuracy (this is the “cost” of differential privacy), it does not compromise utility or data quality compared to traditional data masking techniques.

Data Scientist 88

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phData

JUNE 27, 2023

The modern data stack (MDS) has seen massive changes over the past few decades, fueled by technological advances and new platforms. As a result, we are presented with specialized data platforms, databases, and warehouses. Platform and More dbt is a database deployment & development platform. Proceed as you see fit.

SQL 52

SQL Database Data Quality Data Warehouse 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 Warehousing: Amazon Redshift, Google BigQuery, etc.

Data Engineering 52

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Pickl AI

DECEMBER 4, 2023

Data governance and security Like a fortress protecting its treasures, data governance, and security form the stronghold of practical Data Intelligence. Think of data governance as the rules and regulations governing the kingdom of information. It ensures data quality , integrity, and compliance.

Data Analysis 52

Data Analysis Data Analysis Artificial Intelligence Artificial Intelligence 52

Precisely

MARCH 9, 2023

Processing speeds were considerably slower than they are today, so large volumes of data called for an approach in which data was staged in advance, often running ETL (extract, transform, load) processes overnight to enable next-day visibility to key performance indicators. It is often used as a foundation for enterprise data lakes.

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Data Lakes Data Warehouse Hadoop Apache Hadoop 59

Iguazio

FEBRUARY 20, 2024

This includes management vision and strategy, resource commitment, data and tech and operating model alignment, robust risk management and change management. The third is substituting names and SSNs with masked data. Finally, the data is indexed in the vector database. The fourth is tokenization.

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AI AI Data Pipeline Data Quality 52

Dataconomy

MARCH 24, 2023

Some of the common career opportunities in BI include: Entry-level roles Data analyst:  A data analyst is responsible for collecting and analyzing data, creating reports, and presenting insights to stakeholders. They may also be involved in data modeling and database design.

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

Dataconomy

MARCH 24, 2023

Some of the common career opportunities in BI include: Entry-level roles Data analyst:  A data analyst is responsible for collecting and analyzing data, creating reports, and presenting insights to stakeholders. They may also be involved in data modeling and database design.

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

Pickl AI

JUNE 23, 2023

The following section highlights the key steps of functioning of Data-centric architecture. Data Collection The process begins with the collection of relevant and diverse data from various sources. This can include structured data (e.g., databases, spreadsheets) as well as unstructured data (e.g.,

AI 52

AI AI Data Governance Data Quality 52

Pickl AI

JANUARY 16, 2024

Join me in understanding the pivotal role of Data Analysts , where learning is not just an option but a necessity for success. Key takeaways Develop proficiency in Data Visualization, Statistical Analysis, Programming Languages (Python, R), Machine Learning, and Database Management.

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Pickl AI

MAY 7, 2024

Perform data transformations, such as merging, filtering, and aggregating data Data Analysis and Modeling Analyze data using statistical techniques, data mining, and predictive modeling.

Data Analyst 52

Data Analyst Tableau Data Science Power BI 52

ODSC - Open Data Science

OCTOBER 15, 2023

The platform is used by businesses of all sizes to build and deploy machine learning models to improve their operations. ArangoDB ArangoDB is a company that provides a database platform for graph and document data. Snorkel ai Snorkel AI is a company that provides a platform for building and managing active learning models.

Machine Learning 52

Machine Learning Machine Learning Data Pipeline AI 52

The MLOps Blog

MARCH 15, 2023

If you will ask data professionals about what is the most challenging part of their day to day work, you will likely discover their concerns around managing different aspects of data before they get to graduate to the data modeling stage. This ensures that the data is accurate, consistent, and reliable.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

ODSC - Open Data Science

APRIL 26, 2023

Because of the explosion of data over the last few years, you can expect to see data engineers working in industries such as finance, healthcare, the public sector, e-commerce, and media. As you can imagine, data architects require a strong background in database design, data modeling, and data management.

Data Analyst 52

Data Analyst Machine Learning Machine Learning Power BI 52

Tableau

JANUARY 27, 2021

In 2020, we released some of the most highly-anticipated features in Tableau, including dynamic parameters , new data modeling capabilities , multiple map layers and improved spatial support, predictive modeling functions , and Metrics. We continue to make Tableau more powerful, yet easier to use.

Tableau 70

Tableau Business Intelligence Business Intelligence Analytics 70

phData

AUGUST 10, 2023

Utilize dbt’s incremental materialization to process new feeds from Snowflake streams or implement any intermediary Ephemeral models in dbt to achieve the same. Implement business rules and validations: Data Vault models often involve enforcing business rules and performing data quality checks.

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

Tableau

JANUARY 27, 2021

In 2020, we released some of the most highly-anticipated features in Tableau, including dynamic parameters , new data modeling capabilities , multiple map layers and improved spatial support, predictive modeling functions , and Metrics. We continue to make Tableau more powerful, yet easier to use.

Tableau 52

Tableau Business Intelligence Business Intelligence Analytics 52

phData

MARCH 22, 2023

Sigma Computing’s Metrics are a powerful tool for simplifying this complexity and making it easier for business users to access and understand data. In this blog, we will explore what Metrics are, how they work, and why they should be used in data modeling. What Are Metrics From Sigma? Who can create Metrics in Sigma?

Data Modeling 52

Data Modeling Data Models Data Quality Database 52

Smart Data Collective

JULY 9, 2023

ETL is a three-step process that involves extracting data from various sources, transforming it into a consistent format, and loading it into a target database or data warehouse. Extract The extraction phase involves retrieving data from diverse sources such as databases, spreadsheets, APIs, or other systems.

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ETL Data Quality Data Warehouse Database 85

Pickl AI

JUNE 28, 2023

Schema Integration Schema integration deals with reconciling data stored in different database schemas or structures. It involves mapping and transforming data elements to align with a unified schema. It requires human effort to extract data from each source and merge it.

Data Mining 52

Data Mining Data Mining Data Mining ETL 52

Mlearning.ai

MAY 16, 2023

ETL Design Pattern The ETL (Extract, Transform, Load) design pattern is a commonly used pattern in data engineering. It is used to extract data from various sources, transform the data to fit a specific data model or schema, and then load the transformed data into a target system such as a data warehouse or a database.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

The MLOps Blog

JUNE 27, 2023

Model versioning, lineage, and packaging : Can you version and reproduce models and experiments? Can you see the complete model lineage with data/models/experiments used downstream? Dolt Dolt is an open-source relational database system built on Git. Is it fast and reliable enough for your workflow?

Machine Learning 134

Machine Learning Machine Learning ML ML 134

Precisely

OCTOBER 23, 2023

Read Common Misconceptions About Master Data Management Most people think of MDM as a means of systematically matching and deduplicating records across multiple databases and applications, but modern MDM plays a far more meaningful role. An ERP does not do data quality very well. Here's a MDM checklist you need.

Data Quality 110

Data Quality Data Warehouse Analytics Analytics 110

phData

APRIL 10, 2023

A truly governed self-service analytics model puts data modeling responsibilities in the hands of IT and report generation and analysis in the hands of business users who will actually be doing the analysis. Business users build reports on an IT-owned and IT-created data model that is focused on reporting solutions.

Analytics 52

Analytics Analytics Data Governance Data Modeling 52

Dataversity

MARCH 1, 2021

Data warehouse (DW) testers with data integration QA skills are in demand. Data warehouse disciplines and architectures are well established and often discussed in the press, books, and conferences. Each business often uses one or more data […]. Click to learn more about author Wayne Yaddow.

Data Warehouse 52

Data Warehouse ETL Data Governance Data Quality 52

Alation

AUGUST 16, 2022

But do they empower many user types to quickly find trusted data for a business decision or data model? Many data catalogs suffer from a lack of adoption because they are too technical. And with our Open Connector Framework , customers and partners can easily build connectors to even more data sources.

Data Quality 52

Data Quality Data Governance Cloud Data Data Engineering 52

The MLOps Blog

APRIL 17, 2023

You don’t want to end up in a situation where you need to rewrite a system due to some shortcuts you took early on when only one data scientist was using it. Some will want to track entire pipelines of data transformations, and others will monitor models in production. Of course, a relational database would be valuable here.

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AWS Machine Learning Blog

AUGUST 21, 2023

The capabilities of Lake Formation simplify securing and managing distributed data lakes across multiple accounts through a centralized approach, providing fine-grained access control. Solution overview We demonstrate this solution with an end-to-end use case using a sample dataset, the TPC data model.

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Pickl AI

JULY 23, 2023

It is curated intentionally for a specific purpose, often to analyze and derive insights from the data it contains. Datasets are typically formatted and stored in files, databases, or spreadsheets, allowing for easy access and analysis. Types of Data 1. It follows a specific schema, making it easy to analyze and process.

Analytics 52

Analytics Analytics Predictive Analytics Data Analysis 52

Mlearning.ai

NOVEMBER 29, 2023

GP has intrinsic advantages in data modeling, given its construction in the framework of Bayesian hierarchical modeling and no requirement for a priori information of function forms in Bayesian reference. Decision Trees ML-based decision trees are used to classify items (products) in the database.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Flipboard

JULY 31, 2023

Nowadays, with the advent of deep learning and convolutional neural networks, this process can be automated, allowing the model to learn the most relevant features directly from the data. Model Training: With the labeled data and identified features, the next step is to train a machine learning model.

Machine Learning 104

Machine Learning Machine Learning Deep Learning Deep Learning 104

phData

NOVEMBER 13, 2023

Sidebar Navigation: Provides a catalog sidebar for browsing resources by type, package, file tree, or database schema, reflecting the structure of both dbt projects and the data platform. Version Tracking: Displays version information for models, indicating whether they are prerelease, latest, or outdated.

Database 52

Database Business Intelligence Business Intelligence Data Silos 52

phData

JULY 18, 2023

First up, let’s dive into the foundation of every Modern Data Stack, a cloud-based data warehouse. Central Source of Truth for Analytics A Cloud Data Warehouse (CDW) is a type of database that provides analytical data processing and storage capabilities within a cloud-based infrastructure.

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