The Modern Data Company

MAY 8, 2023

Potential downsides of data lakes include governance and integration challenges. Data lakes often lack robust data governance, leading to data quality, consistency, and security issues. It provides a flexible, scalable, and secure data infrastructure that can adapt to evolving business needs.

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phData: Data Engineering

SEPTEMBER 19, 2023

This blog will guide you through the best data modeling methodologies and processes for your data lake, helping you make informed decisions and optimize your data management practices. What is a Data Lake? What are Data Modeling Methodologies, and Why Are They Important for a Data Lake? Contact phData Today!

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Data Lake Process Metadata Data Warehouse 52

Knowledge Hut

APRIL 23, 2024

Big Data Training online courses will help you build a robust skill-set working with the most powerful big data tools and technologies. Big Data vs Small Data: Velocity Big Data is often characterized by high data velocity, requiring real-time or near real-time data ingestion and processing.

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Big Data Datasets Data Analysis Media 52

Snowflake

NOVEMBER 29, 2023

While the former can be solved by tokenization strategies provided by external vendors, the latter mandates the need for patient-level data enrichment to be performed with sufficient guardrails to protect patient privacy, with an emphasis on auditability and lineage tracking. A conceptual architecture illustrating this is shown in Figure 3.

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Metadata Healthcare Medical Data Storage 84

Knowledge Hut

MARCH 28, 2024

Job Role 1: Azure Data Engineer Azure Data Engineers develop, deploy, and manage data solutions with Microsoft Azure data services. They use many data storage, computation, and analytics technologies to develop scalable and robust data pipelines. GDPR, HIPAA), and industry standards.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

Monte Carlo

AUGUST 19, 2023

We’ll cover: What is a data platform? To make things a little easier, I’ve outlined the six must-have layers you need to include in your data platform and the order in which many of the best teams choose to implement them. The five must-have layers of a modern data platform Second to “how do I build my data platform?”,

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Building BI Data Lake Data Governance 52

Knowledge Hut

NOVEMBER 2, 2023

An Azure Data Engineer is a professional who is in charge of designing, implementing, and maintaining data processing systems and solutions on the Microsoft Azure cloud platform. A Data Engineer is responsible for designing the entire architecture of the data flow while taking the needs of the business into account.

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Data Engineering Data Engineer Coding Project 52

Knowledge Hut

MARCH 20, 2024

An Azure Data Engineer is a professional specializing in designing, implementing, and managing data solutions on the Microsoft Azure cloud platform. They possess expertise in various aspects of data engineering. As an Azure data engineer myself, I was responsible for managing data storage, processing, and analytics.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

Knowledge Hut

MARCH 15, 2024

An Azure Data Engineer is a professional specializing in designing, implementing, and managing data solutions on the Microsoft Azure cloud platform. They possess expertise in various aspects of data engineering. As an Azure data engineer myself, I was responsible for managing data storage, processing, and analytics.

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Data Engineering Data Engineer Engineering Data Governance 52

AltexSoft

AUGUST 29, 2023

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data.

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Data Lake Architecture IT Amazon Web Services 52

AltexSoft

MARCH 14, 2023

Batch jobs are often scheduled to load data into the warehouse, while real-time data processing can be achieved using solutions like Apache Kafka and Snowpipe by Snowflake to stream data directly into the cloud warehouse. But this distinction has been blurred with the era of cloud data warehouses.

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IT Data Warehouse Data Governance Data Lake 59

Edureka

FEBRUARY 7, 2023

This exam measures your ability to design and implement data management, data processing, and data security solutions using Azure data services. The course covers the skills and knowledge required to design and implement data management, data processing, and data security solutions using Azure data services.

Data Engineering 52

Data Engineering Data Engineer Engineering Programming Language 52

Knowledge Hut

JANUARY 3, 2024

Using Azure services, they create, manage, and optimize data pipelines, databases, and data warehouses. Data Engineers on the Azure platform work with big data technologies , establish data governance and assure data security, integrity, and availability.

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Cloud Computing Recruitment Computer Science Education 52

AltexSoft

MAY 12, 2023

Tools and platforms for unstructured data management Unstructured data collection Unstructured data collection presents unique challenges due to the information’s sheer volume, variety, and complexity. The process requires extracting data from diverse sources, typically via APIs. Data durability and availability.

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Unstructured Data NoSQL Hadoop Data Lake 59

AltexSoft

JUNE 26, 2023

Read our article on Hotel Data Management to have a full picture of what information can be collected to boost revenue and customer satisfaction in hospitality. While all three are about data acquisition, they have distinct differences. Find sources of relevant data. Choose data collection methods and tools.

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Data Collection Machine Learning Unstructured Data Non-relational Database 52

Data Engineering Weekly

NOVEMBER 13, 2022

link] Meta: Tulip - Schematizing Meta’s data platform Numerous heterogeneous services make up a data platform, such as warehouse data storage and various real-time systems. The schematization of data plays a vital role in a data platform. The author shares the experience of one such transition.

Data Engineering 52

Data Engineering Data Engineer Engineering Kafka 52

Monte Carlo

JUNE 14, 2023

Why is data pipeline architecture important? This is frequently referred to as a 5 or 7 layer (depending on who you ask) data stack like in the image below. Here are some of the most common solutions that are involved in modern data pipelines and the role they play.

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phData: Data Engineering

AUGUST 4, 2023

Data storage is a vital aspect of any Snowflake Data Cloud database. Within Snowflake, data can either be stored locally or accessed from other cloud storage systems. Amazon S3 for AWS, Azure Blob Storage for Azure, or Google Cloud Storage for GCP) to store the actual data files in micro-partitions.

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Cloud Storage Google Cloud Amazon Web Services Data Storage 52

Knowledge Hut

APRIL 25, 2023

It provides a unified API that allows businesses to collect customer data from various sources, such as websites, mobile apps, and servers. Key features: Centralized customer data Real-time data streaming Support for data governance 6. It can add more processing power and storage as the data grows.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

Monte Carlo

APRIL 24, 2023

Data lakes are useful, flexible data storage repositories that enable many types of data to be stored in its rawest state. Notice how Snowflake dutifully avoids (what may be a false) dichotomy by simply calling themselves a “data cloud.”

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Data Lake Google Cloud Data Warehouse AWS 52

Cloudera

MAY 9, 2019

It is meant for you to assess if you have thought through processes such as continuous data ingestion, enterprise data integration and data governance. Data infrastructure readiness – IoT architectures can be insanely complex and sophisticated.

Manufacturing 54

Manufacturing Data Ingestion Architecture Data Governance 54

Snowflake

MARCH 30, 2023

3EJHjvm Once a business need is defined and a minimal viable product ( MVP ) is scoped, the data management phase begins with: Data ingestion: Data is acquired, cleansed, and curated before it is transformed. Feature engineering: Data is transformed to support ML model training. ML workflow, ubr.to/3EJHjvm

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Engineering Raw Data Data Science Scala 83

phData: Data Engineering

JANUARY 3, 2022

This means it’s business-critical that companies can derive value from their data to better inform business decisions, protect their enterprise and their customers, and grow their business. This comprehensive guide will cover all of the basics of data engineering including common roles, functions, and responsibilities.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

ProjectPro

FEBRUARY 21, 2023

GCP Data Engineer Certification The Google Cloud Certified Professional Data Engineer certification is ideal for data professionals whose jobs generally involve data governance, data handling, data processing, and performing a lot of feature engineering on data to prepare it for modeling.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

AltexSoft

JULY 29, 2022

No matter the actual size, each cluster accommodates three functional layers — Hadoop distributed file systems for data storage, Hadoop MapReduce for processing, and Hadoop Yarn for resource management. Today, Hadoop which combines data storage and processing capabilities remains a basis for many Big Data projects.

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Hadoop Big Data Google Cloud NoSQL 59

ProjectPro

NOVEMBER 15, 2021

It was built from the ground up for interactive analytics and can scale to the size of Facebook while approaching the speed of commercial data warehouses. Presto allows you to query data stored in Hive, Cassandra, relational databases, and even bespoke data storage.

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Big Data Project Metadata Programming Language 52

phData: Data Engineering

JULY 18, 2023

Zero Copy Cloning: Create multiple ‘copies’ of tables, schemas, or databases without actually copying the data. This noticeably saves time on copying and drastically reduces data storage costs. Even things like data access reviews are typically done manually without automation.

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Data Warehouse Pipeline-centric Government Data 52