Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

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AWS Machine Learning Blog

OCTOBER 20, 2023

Data and governance foundations – This function uses a data mesh architecture for setting up and operating the data lake, central feature store, and data governance foundations to enable fine-grained data access.

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ML ML AWS Data Lakes 131

Alation

FEBRUARY 17, 2023

This past week, I had the pleasure of hosting Data Governance for Dummies author Jonathan Reichental for a fireside chat , along with Denise Swanson , Data Governance lead at Alation. Can you have proper data management without establishing a formal data governance program?

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Data Governance Data Quality Data Analyst Data Pipeline 52

ODSC - Open Data Science

JANUARY 18, 2024

Data engineering is a rapidly growing field, and there is a high demand for skilled data engineers. If you are a data scientist, you may be wondering if you can transition into data engineering. In this blog post, we will discuss how you can become a data engineer if you are a data scientist.

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Data Engineering Data Engineering Data Engineering Data Engineer 52

phData

NOVEMBER 9, 2023

The main goal of a data mesh structure is to drive: Domain-driven ownership Data as a product Self-service infrastructure Federated governance One of the primary challenges that organizations face is data governance. What is a Data Lake? Today, data lakes and data warehouses are colliding.

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Data Warehouse Data Lakes Clustering Cloud Data 98

AWS Machine Learning Blog

JUNE 5, 2023

Many teams are turning to Athena to enable interactive querying and analyze their data in the respective data stores without creating multiple data copies. Athena allows applications to use standard SQL to query massive amounts of data on an S3 data lake. Create a data lake with Lake Formation.

Machine Learning 95

Machine Learning Machine Learning AWS Data Lakes 95

Alation

APRIL 6, 2023

Data governance is traditionally applied to structured data assets that are most often found in databases and information systems. The ability to connect straight to the source allows knowledge workers to work natively in spreadsheets, pulling data directly from true data sources like the data warehouse or data lake.

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Alation

JANUARY 13, 2022

Thoughtworks says data mesh is key to moving beyond a monolithic data lake. Spoiler alert: data fabric and data mesh are independent design concepts that are, in fact, quite complementary. Thoughtworks says data mesh is key to moving beyond a monolithic data lake 2. Gartner on Data Fabric.

Data Lakes 59

Data Lakes Data Governance Data Quality Data Warehouse 59

Tableau

FEBRUARY 14, 2022

“I think one of the most important things I see people do right, is to make sure that you build the data foundation from the ground up correctly,” said Ali Ghodsi, CEO of Databricks. The data lakehouse is one such architecture—with “lake” from data lake and “house” from data warehouse.

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AI AI Tableau Data Scientist 98

Alation

MAY 16, 2023

Data Storage The data storage component of a pipeline provides secure, scalable storage for the data. Various data storage methods are available, including data warehouses for structured data or data lakes for unstructured, semi-structured, and structured data.

Data Pipeline 69

Data Pipeline Data Governance Data Lakes Data Warehouse 69

Tableau

FEBRUARY 14, 2022

“I think one of the most important things I see people do right, is to make sure that you build the data foundation from the ground up correctly,” said Ali Ghodsi, CEO of Databricks. The data lakehouse is one such architecture—with “lake” from data lake and “house” from data warehouse.

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AI AI Tableau Data Scientist 95

AWS Machine Learning Blog

FEBRUARY 13, 2024

Let’s demystify this using the following personas and a real-world analogy: Data and ML engineers (owners and producers) – They lay the groundwork by feeding data into the feature store Data scientists (consumers) – They extract and utilize this data to craft their models Data engineers serve as architects sketching the initial blueprint.

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AWS ML ML Machine Learning 128

IBM Journey to AI blog

JANUARY 5, 2023

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

Data Quality 52

Data Quality Data Lakes Data Warehouse Big Data 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.

Data Lakes 52

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Alation

JUNE 21, 2022

The audience grew to include data scientists (who were even more scarce and expensive) and their supporting resources (e.g., After that came data governance , privacy, and compliance staff. Power business users and other non-purely-analytic data citizens came after that. Data engineers want to catalog data pipelines.

DataOps 52

DataOps Data Scientist Data Quality Data Pipeline 52

IBM Journey to AI blog

APRIL 25, 2023

In a prior blog , we pointed out that warehouses, known for high-performance data processing for business intelligence, can quickly become expensive for new data and evolving workloads. To do so, Presto and Spark need to readily work with existing and modern data warehouse infrastructures.

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Data Warehouse Data Engineering Data Engineering Data Engineering 58

IBM Journey to AI blog

DECEMBER 16, 2022

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization. This is especially helpful when handling massive amounts of big data.

Data Quality 52

Data Quality Data Governance Data Scientist Data Wrangling 52

Alation

FEBRUARY 13, 2020

Expansion in our business model is driven by the number of users of the data catalog, which means that our average customer is virally successful relative to their initial investment. The Alation Data Catalog is taking years of data lake and self-service analytics investments and driving them from investments to insights.

Data Lakes 52

Data Lakes Analytics Analytics Machine Learning 52

Alation

APRIL 18, 2023

Top use cases for data profiling Data governance Data governance describes how data should be gathered and used within an organization, impacting data quality, data security, data privacy , and compliance. Do you need to define a data quality rule and add that to the profile?

Data Profiling 52

Data Profiling Data Quality Data Governance Data Pipeline 52

Alation

MARCH 22, 2022

Accenture calls it the Intelligent Data Foundation (IDF), and it’s used by dozens of enterprises with very complex data landscapes and analytic requirements. Simply put, IDF standardizes data engineering processes. They can better understand data transformations, checks, and normalization.

DataOps 52

DataOps Data Pipeline Data Engineering Data Engineering 52

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

Alation

AUGUST 11, 2022

Today, the brightest minds in our industry are targeting the massive proliferation of data volumes and the accompanying but hard-to-find value locked within all that data. Data mesh says architectures should be decentralized because there are inherent problems with centralized architectures. Subscribe to Alation's Blog.

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Data Warehouse Data Engineering Data Engineering Data Engineering 52

IBM Journey to AI blog

MARCH 14, 2024

. Netezza incorporates in-database analytics and machine learning (ML), governance, security and patented massively parallel processing. Whether it’s for ad hoc analytics, data transformation, data sharing, data lake modernization or ML and gen AI, you have the flexibility to choose.

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The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. This provides end-to-end support for data engineering and MLOps workflows.

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Machine Learning Machine Learning ML ML 134

Alation

MAY 16, 2023

Data Storage The data storage component of a pipeline provides secure, scalable storage for the data. Various data storage methods are available, including data warehouses for structured data or data lakes for unstructured, semi-structured, and structured data.

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AWS Machine Learning Blog

FEBRUARY 7, 2025

Data Governance Account This account hosts data governance services for data lake, central feature store, and fine-grained data access. The SageMaker Project Portfolio has SageMaker projects that data scientists and ML engineers can use to accelerate model training and deployment.

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ML ML Data Scientist AWS 63

Alation

MARCH 1, 2022

Through Impact Analysis, users can determine if a problem occurred with data upstream, and locate the impacted data downstream. With robust data lineage, data engineers can find and fix issues fast and prevent them from recurring. Similarly, analysts gain a clear view of how data is created. In 2022.1,

Data Warehouse 52

Data Warehouse Data Lakes Cloud Data Database 52

phData

SEPTEMBER 20, 2023

A data mesh is a conceptual architectural approach for managing data in large organizations. Traditional data management approaches often involve centralizing data in a data warehouse or data lake, leading to challenges like data silos, data ownership issues, and data access and processing bottlenecks.

Data Silos 52

Data Silos Database Data Quality Data Engineering 52

Pickl AI

OCTOBER 10, 2024

Support for Advanced Analytics : Transformed data is ready for use in Advanced Analytics, Machine Learning, and Business Intelligence applications, driving better decision-making. Compliance and Governance : Many tools have built-in features that ensure data adheres to regulatory requirements, maintaining data governance across organisations.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52

Alation

NOVEMBER 28, 2022

But refreshing this analysis with the latest data was impossible… unless you were proficient in SQL or Python. We wanted to make it easy for anyone to pull data and self service without the technical know-how of the underlying database or data lake. Sathish and I met in 2004 when we were working for Oracle.

Data Governance 52

Data Governance Database Data Quality Data Lakes 52

Alation

JULY 7, 2022

To answer these questions we need to look at how data roles within the job market have evolved, and how academic programs have changed to meet new workforce demands. In the 2010s, the growing scope of the data landscape gave rise to a new profession: the data scientist. programs in Information Science and Data Analytics.

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Data Scientist Data Analyst Analytics Analytics 52

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. Data Security and Governance Maintaining data security is crucial for any company.

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Data Warehouse Business Intelligence Business Intelligence Database 52

Dataversity

DECEMBER 8, 2021

As part of a well-desired culture change of data awareness in an organization, data democratization is a concept that enables easy access to data by anyone. The ease of availability and access to data allows for direct and indirect data monetization, thus improving revenue streams.

Analytics 52

Analytics Analytics Data Lakes Data Quality 52

Data Science Blog

MAY 15, 2023

tl;dr Ein Data Lakehouse ist eine moderne Datenarchitektur, die die Vorteile eines Data Lake und eines Data Warehouse kombiniert. Die Definition eines Data Lakehouse Ein Data Lakehouse ist eine moderne Datenspeicher- und -verarbeitungsarchitektur, die die Vorteile von Data Lakes und Data Warehouses vereint.

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Data Science Blog

JULY 20, 2024

Die Bedeutung effizienter und zuverlässiger Datenpipelines in den Bereichen Data Science und Data Engineering ist enorm. Automatisierung: Erstellt SQL-Code, DACPAC-Dateien, SSIS-Pakete, Data Factory-ARM-Vorlagen und XMLA-Dateien. Data Lakes: Unterstützt MS Azure Blob Storage.

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Data Science Blog

MARCH 14, 2023

Von Big Data über Data Science zu AI Einer der Gründe, warum Big Data insbesondere nach der Euphorie wieder aus der Diskussion verschwand, war der Leitspruch “S**t in, s**t out” und die Kernaussage, dass Daten in großen Mengen nicht viel wert seien, wenn die Datenqualität nicht stimme.

Big Data 147

Big Data Big Data Apache Hadoop Data Science 147

phData

SEPTEMBER 27, 2024

Both persistent staging and data lakes involve storing large amounts of raw data. But persistent staging is typically more structured and integrated into your overall customer data pipeline. Building a composable CDP requires some serious data engineering chops. Looking for purchase data?

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Data Modeling Data Models Apache Kafka Data Lakes 52