Data Science Blog

MAY 15, 2023

Darüber hinaus können Data Governance- und Sicherheitsrichtlinien auf die Daten in einem Data Lakehouse angewendet werden, um die Datenqualität und die Einhaltung von Vorschriften zu gewährleisten. Wenn Ihre Analyse jedoch eine gewisse Latenzzeit tolerieren kann, könnte ein Data Warehouse die bessere Wahl sein.

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Precisely

DECEMBER 20, 2022

Within the financial industry, there are some specialized uses for data integration and big data analytics. Many institutions need to access key customer data from mainframe applications and integrate that data with Hadoop and Spark to power advanced insights. Data governance provides the answer.

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Alation

FEBRUARY 20, 2020

Experts who understand certain datasets often play the stewardship role of ensuring that data consumers can make accurate and effective use of data. More recently, data governance initiatives have started to assign formal stewardship responsibility. How recently the data was used. How recently the data was updated.

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Precisely

MARCH 9, 2023

As cloud computing platforms make it possible to perform advanced analytics on ever larger and more diverse data sets, new and innovative approaches have emerged for storing, preprocessing, and analyzing information. Hadoop, Snowflake, Databricks and other products have rapidly gained adoption. They can be changed, but not easily.

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Smart Data Collective

APRIL 24, 2023

Innovations in the early 20th century changed how data could be used. Google’s Hadoop allowed for unlimited data storage on inexpensive servers, which we now call the Cloud. Data brokers have over 3,000 profiles on each individual, including personal information like political preferences and hobbies.

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Pickl AI

DECEMBER 2, 2024

Data lakes and cloud storage provide scalable solutions for large datasets. Processing frameworks like Hadoop enable efficient data analysis across clusters. Analytics tools help convert raw data into actionable insights for businesses. Strong data governance ensures accuracy, security, and compliance in data management.

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Pickl AI

NOVEMBER 25, 2024

Data lakes and cloud storage provide scalable solutions for large datasets. Processing frameworks like Hadoop enable efficient data analysis across clusters. Analytics tools help convert raw data into actionable insights for businesses. Strong data governance ensures accuracy, security, and compliance in data management.

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Pickl AI

NOVEMBER 4, 2024

Key Takeaways Data Engineering is vital for transforming raw data into actionable insights. Key components include data modelling, warehousing, pipelines, and integration. Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering?

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ODSC - Open Data Science

APRIL 28, 2023

As much as data quality is critical for AI, AI is critical for ensuring data quality, and for reducing the time to prepare data with automation. Data quality also works hand in hand with data governance.

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Dataconomy

FEBRUARY 23, 2023

Solutions for managing and processing large volumes of data Data engineers can use various solutions to manage and process large volumes of data. This approach allows for faster and more efficient processing of large volumes of data.

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Smart Data Collective

MAY 20, 2019

GDPR helped to spur the demand for prioritized data governance , and frankly, it happened so fast it left many companies scrambling to comply — even still some are fumbling with the idea. Data processing is another skill vital to staying relevant in the analytics field. The Rise of Regulation.

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ODSC - Open Data Science

SEPTEMBER 27, 2023

This allows data scientists, analysts, and other stakeholders to perform exploratory analyses and derive insights without prior knowledge of the data structure. This is particularly advantageous when dealing with exponentially growing data volumes.

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Pickl AI

AUGUST 9, 2024

Big Data Technologies and Tools A comprehensive syllabus should introduce students to the key technologies and tools used in Big Data analytics. Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers.

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Pickl AI

SEPTEMBER 17, 2024

Technologies and Tools for Big Data Management To effectively manage Big Data, organisations utilise a variety of technologies and tools designed specifically for handling large datasets. This section will highlight key tools such as Apache Hadoop, Spark, and various NoSQL databases that facilitate efficient Big Data management.

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Pickl AI

JULY 25, 2023

They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently.

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Alation

FEBRUARY 13, 2020

By leveraging Google-like smart search to find data assets; using automation and self-learning instead of burdening people with the need to manually update metadata in multiple places; and ensuring that metadata is maintained by the whole data community and is not dependent on a centralized IT team.

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DagsHub

AUGUST 23, 2024

We already know that a data quality framework is basically a set of processes for validating, cleaning, transforming, and monitoring data. Data Governance Data governance is the foundation of any data quality framework. It primarily caters to large organizations with complex data environments.

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Pickl AI

SEPTEMBER 11, 2024

Platform as a Service (PaaS) PaaS offerings provide a development environment for building, testing, and deploying Big Data applications. This layer includes tools and frameworks for data processing, such as Apache Hadoop, Apache Spark, and data integration tools.

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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.

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ODSC - Open Data Science

APRIL 26, 2023

As models become more complex and the needs of the organization evolve and demand greater predictive abilities, you’ll also find that machine learning engineers use specialized tools such as Hadoop and Apache Spark for large-scale data processing and distributed computing.

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Alation

FEBRUARY 20, 2020

As a cornerstone of your data architecture the EDM is a serious undertaking whether it is enabled by building on existing technologies or by deploying a single tool that includes all of the functions needed to successfully implement one. Get the latest data cataloging news and trends in your inbox.

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Pickl AI

NOVEMBER 15, 2023

However, there lies a difference between the two: Read Blog: Difference B/w Data Warehouse and Database Feature Database System Data Warehouse Purpose Transactional processing, day-to-day operations Analytical processing, decision support Data Type Current and operational data Historical and analytical data Data Structure Structured data Structured (..)

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Pickl AI

NOVEMBER 5, 2024

Users can connect to live data or extract data for analysis, giving flexibility to those with extensive and complex datasets. Tableau’s data connectors include Salesforce, Google Analytics, Hadoop, Amazon Redshift, and others catering to enterprise-level data needs.

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Pickl AI

JULY 30, 2024

Organizations can monitor the lineage of data as it moves through the system, providing visibility into data transformations and ensuring compliance with data governance policies.

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DagsHub

OCTOBER 23, 2024

They enable flexible data storage and retrieval for diverse use cases, making them highly scalable for big data applications. Popular data lake solutions include Amazon S3 , Azure Data Lake , and Hadoop. Data Processing Tools These tools are essential for handling large volumes of unstructured data.

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Alation

JANUARY 25, 2022

This is a key component of active data governance. These capabilities are also key for a robust data fabric. Another key nuance of a data fabric is that it captures social metadata. Social metadata captures the associations that people create with the data they produce and consume. The Power of Social Metadata.

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Pickl AI

JANUARY 12, 2025

Moreover, regulatory requirements concerning data utilisation, like the EU’s General Data Protection Regulation GDPR, further complicate the situation. Such challenges can be mitigated by durable data governance, continuous training, and high commitment toward ethical standards.

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Alation

FEBRUARY 20, 2020

Alation then adds rich behavioral context to this inventory: Just as a consumer catalog like Yelp can indicate the popularity of a particular restaurant, Alation can tell you the popularity of the data underlying a MicroStrategy report or dashboard. Get the latest data cataloging news and trends in your inbox.

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IBM Journey to AI blog

JULY 5, 2023

The challenges of a monolithic data lake architecture Data lakes are, at a high level, single repositories of data at scale. Data may be stored in its raw original form or optimized into a different format suitable for consumption by specialized engines. Data governance remains an unexplored frontier for this technology.

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Pickl AI

APRIL 2, 2024

Tableau/Power BI: Visualization tools for creating interactive and informative data visualizations. Hadoop/Spark: Frameworks for distributed storage and processing of big data. Cloud Platforms (AWS, Azure, Google Cloud): Infrastructure for scalable and cost-effective data storage and analysis.

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DataRobot Blog

OCTOBER 3, 2017

Additionally, Alation and Paxata announced the new data exploration capabilities of Paxata in the Alation Data Catalog, where users can find trusted data assets and, with a single click, work with their data in Paxata’s Self-Service Data Prep Application.

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