Cloudera

JANUARY 28, 2022

Then there are the more extensive discussions – scrutiny of the overarching, data strategy questions related to privacy, security, data governance /access and regulatory oversight. These are not straightforward decisions, especially when data breaches always hit the top of the news headlines.

Cloud 111

Cloud Banking Data Governance Government 111

Cloudera

JUNE 22, 2023

The top-line benefits of a hybrid data platform include: Cost efficiency. A hybrid data platform enables the preservation of existing investments in legacy applications and workloads without modifying them. It ensures compliance with regulatory requirements while shifting non-sensitive data and workloads to the cloud.

Data Cleanse 65

Data Cleanse Data Governance Unstructured Data Cloud Storage 65

Databand.ai

JULY 19, 2023

Data lineage tools are not a new concept. However, their importance has grown significantly in recent years due to the increasing complexity of data architectures and the growing need for data governance and compliance. In this article: Why Are Data Lineage Tools Important?

Pipeline-centric 52

Pipeline-centric Data Governance Metadata Government 52

Knowledge Hut

APRIL 23, 2024

Big Data certification course will support you in learning big data skills from the greatest mentors to help you build a career in big data. Top 10 Disadvantages of Big Data 1. Need for Skilled Personnel We see data in different forms; it can be categorized into structured, semi-structured, and unstructured data.

Big Data 52

Big Data Media Government Big Data Skills 52

The Modern Data Company

MAY 10, 2023

Data governance and security: Evaluate the native security, data governance, and data quality management features. Because data lakes can have performance limitations for these use cases, a data warehouse may be a better fit. Data warehouses offer the optimized performance you need.

Data Management 52

Data Management Data Lake Management Data Warehouse 52

The Modern Data Company

MAY 10, 2023

Data governance and security: Evaluate the native security, data governance, and data quality management features. Because data lakes can have performance limitations for these use cases, a data warehouse may be a better fit. Data warehouses offer the optimized performance you need.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

The Modern Data Company

MAY 10, 2023

Data governance and security: Evaluate the native security, data governance, and data quality management features. Because data lakes can have performance limitations for these use cases, a data warehouse may be a better fit. Data warehouses offer the optimized performance you need.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

Cloudera

DECEMBER 11, 2020

As mentioned in my previous blog on the topic , the recent shift to remote working has seen an increase in conversations around how data is managed. Toolsets and strategies have had to shift to ensure controlled access to data. At the same time, the need to have a strong layer of security and governance is being highlighted.

Data Lake 98

Data Lake Government Data Security Unstructured Data 98

Knowledge Hut

MARCH 28, 2024

Role Level Advanced Responsibilities Design and architect data solutions on Azure, considering factors like scalability, reliability, security, and performance. Develop data models, data governance policies, and data integration strategies. GDPR, HIPAA), and industry standards.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

Monte Carlo

FEBRUARY 21, 2023

A data fabric isn’t a standalone technology—it’s a data management architecture that leverages an integrated data layer atop underlying data in order to empower business leaders with real-time analytics and data-driven insights. That’s a model worth looking at when it comes to data governance,” says Bob.

Data Architecture 52

Data Architecture Architecture Metadata Unstructured Data 52

Monte Carlo

FEBRUARY 21, 2023

A data fabric isn’t a standalone technology—it’s a data management architecture that leverages an integrated data layer atop underlying data in order to empower business leaders with real-time analytics and data-driven insights. That’s a model worth looking at when it comes to data governance,” says Bob.

Data Architecture 52

Data Architecture Architecture Metadata Unstructured Data 52

Precisely

MARCH 9, 2023

We will also address some of the key distinctions between platforms like Hadoop and Snowflake, which have emerged as valuable tools in the quest to process and analyze ever larger volumes of structured, semi-structured, and unstructured data. Precisely helps enterprises manage the integrity of their data.

Data Lake 59

Data Lake Data Warehouse Hadoop Raw Data 59

phData: Data Engineering

SEPTEMBER 19, 2023

With the amount of data companies are using growing to unprecedented levels, organizations are grappling with the challenge of efficiently managing and deriving insights from these vast volumes of structured and unstructured data. Want to learn more about data governance?

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

DareData

JULY 5, 2023

We've seen this happen in dozens of our customers: data lakes serve as catalysts that empower analytical capabilities. If you work at a relatively large company, you've seen this cycle happening many times: Analytics team wants to use unstructured data on their models or analysis. And what is the reason for that?

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

Monte Carlo

APRIL 24, 2023

Amazon S3 and/or Lake Formation Amazon S3 is a popular storage platform to build and store data lakes thanks to its high availability and low latency access. S3 has seamless integration with the AWS ecosystem including AWS Glue for data cataloging, Amazon Athena for ad-hoc querying, and Amazon Redshift for data warehousing.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

Knowledge Hut

JULY 26, 2023

These datasets typically involve high volume, velocity, variety, and veracity, which are often referred to as the 4 v's of Big Data: Volume: Volume refers to the vast amount of data generated and collected from various sources. Managing and analyzing such large volumes of data requires specialized tools and technologies.

Big Data 52

Big Data Data Cleanse Retail Healthcare 52

AltexSoft

AUGUST 29, 2023

Unstructured data sources. This category includes a diverse range of data types that do not have a predefined structure. Examples of unstructured data can range from sensor data in the industrial Internet of Things (IoT) applications, videos and audio streams, images, and social media content like tweets or Facebook posts.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

AltexSoft

FEBRUARY 11, 2023

What’s more, investing in data products, as well as in AI and machine learning was clearly indicated as a priority. This suggests that today, there are many companies that face the need to make their data easily accessible, cleaned up, and regularly updated. This privacy law must be kept in mind when building data architecture.

Data Architect 87

Data Architect Certification Generalist Big Data 87

Precisely

SEPTEMBER 6, 2023

.” Poor data quality impedes the success of data programs, hampers data integration efforts, limits data integrity causing big data governance challenges. To truly succeed in an increasingly data-driven world, organizations need data governance. The results are clear.

Data Governance 52

Data Governance Big Data Government Unstructured Data 52

Knowledge Hut

JANUARY 30, 2024

Structured Data: Structured data sources, such as databases and spreadsheets, often require extraction to consolidate, transform, and make them suitable for analysis. Unstructured Data: Unstructured data, like free-form text, can be challenging to work with but holds valuable insights.

ETL Tools 52

ETL Tools Database-centric Data Mining Data Cleanse 52

Monte Carlo

AUGUST 31, 2023

Your downstream data consumers including product analysts, marketing leaders, and sales teams rely on data-driven tools like CRMs, CXPs, CMSs, and any other acronym under the sun to do their jobs quickly and effectively. But what happens when the data is wrong?

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Cloudera

SEPTEMBER 15, 2022

Mark: While most discussions of modern data platforms focus on comparing the key components, it is important to understand how they all fit together. The collection of source data shown on your left is composed of both structured and unstructured data from the organization’s internal and external sources.

Data Lake 79

Data Lake Analytics Application Cloud Storage Architecture 79

Knowledge Hut

JUNE 27, 2023

For example, it can enable remote access to patient records in healthcare, provide online learning platforms for education, and offer affordable data storage & processing in finance. Cybersecurity Overview: Cybersecurity is protection of computer systems and networks from unauthorized access, attacks or malicious activities.

Computer Science 52

Computer Science Entertainment Healthcare Finance 52

Knowledge Hut

MARCH 26, 2024

Responsibilities: Define data architecture strategies and roadmaps to support business objectives and data initiatives. Design data models, schemas, and storage solutions for structured and unstructured data. Evaluate and recommend data management tools, database technologies, and analytics platforms.

Amazon Web Services 52

Amazon Web Services Google Cloud Computer Science AWS 52

Edureka

FEBRUARY 7, 2023

They should also be comfortable working with a variety of data sources and types and be able to design and implement data pipelines that can handle structured, semi-structured, and unstructured data.

Data Engineering 52

Data Engineering Data Engineer Engineering Programming Language 52

Cloudera

MAY 14, 2024

This form of hybrid also goes a level deeper than one may find in a standard hybrid cloud, accounting for the entirety of the data lifecycle, whether that’s the point of ingestion, warehousing, or machine learning—even when that end-to-end data lifecycle is split between entirely different environments. Data comes in many forms.

Cloud 86

Cloud Data Governance Unstructured Data Data Architecture 86

ProjectPro

FEBRUARY 21, 2023

Although challenging, a career in data engineering can be rewarding. Data engineers and their skills play a crucial role in the success of an organization by making it easier for data scientists , data analysts , and decision-makers to access the data they need to do their jobs.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Monte Carlo

MAY 14, 2024

Depending on the quantity of data flowing through an organization’s pipeline — or the format the data typically takes — the right modern table format can help to make workflows more efficient, increase access, extend functionality, and even offer new opportunities to activate your unstructured data.

Data Lake 52

Data Lake Metadata Hadoop Data Governance 52

Cloudera

FEBRUARY 15, 2024

Some of the key benefits of a modern data architecture for regulatory compliance include: Enhanced data governance and compliance: Modern data architecture incorporates data governance practices and security controls to ensure data privacy, regulatory compliance, and protection against unauthorized access or breaches.

Insurance 84

Insurance Banking Data Architecture Data Ingestion 84

Cloudera

OCTOBER 5, 2023

Your organization is not alone — many organizations struggle to move towards data as the cornerstone of their organization. Here are five challenges that you need to overcome to become a data leader: Bad data governance Your insights are only as good as your data.

Data Governance 80

Data Governance Unstructured Data Government High Quality Data 80

Knowledge Hut

DECEMBER 22, 2023

Statistics are used by data scientists to collect, assess, analyze, and derive conclusions from data, as well as to apply quantifiable mathematical models to relevant variables. Microsoft Excel An effective Excel spreadsheet will arrange unstructured data into a legible format, making it simpler to glean insights that can be used.

Hadoop 98

Hadoop Deep Learning Data Science Machine Learning 98

Knowledge Hut

APRIL 25, 2024

Data Integration Data integration is the process of combining information from several sources to give people a cohesive perspective. The fundamental idea behind data integration is to open up data and make it simpler for individuals and systems to access, use, and process.

Big Data 52

Big Data Data Analytics Manufacturing Retail 52

Snowflake

NOVEMBER 29, 2023

In medicine, lower sequencing costs and improved clinical access to NGS technology has been shown to increase diagnostic yield for a range of diseases, from relatively well-understood Mendelian disorders, including muscular dystrophy and epilepsy , to rare diseases such as Alagille syndrome.

Metadata 84

Metadata Healthcare Medical Data Storage 84

Snowflake

SEPTEMBER 21, 2023

Leaders across the Modern Marketing Data Stack are responding to these challenges and differentiating their products by giving brands more access to and control of data. The data driving the provider’s application is stored and processed in the provider’s own Snowflake account.

Google Cloud 85

Google Cloud Unstructured Data Technology Data 85

Monte Carlo

JANUARY 16, 2024

Data lake and data warehouse convergence The data lake vs data warehouse question is constantly evolving. The maxim that data warehouses hold structured data while data lakes hold unstructured data is quickly breaking down. How will data governance be handled?

Data Lake 52

Data Lake Data Warehouse Unstructured Data AWS 52

The Modern Data Company

MAY 8, 2023

One of the most interesting features of a data lake is the “schema-on-read” principle, which means the data schema (the structure and organization of the data) is applied when the data is read or accessed rather than stored.

Data Management 52

Data Management Data Lake Management Data Governance 52

The Modern Data Company

MAY 8, 2023

One of the most interesting features of a data lake is the “schema-on-read” principle, which means the data schema (the structure and organization of the data) is applied when the data is read or accessed rather than stored.

Data Management 52

Data Management Data Lake Management Data Governance 52