Cloudera

JUNE 25, 2021

This blog post outlines detailed step by step instructions to perform Hive Replication from an on-prem CDH cluster to a CDP Public Cloud Data Lake. CDP Data Lake cluster versions – CM 7.4.0, CDP Data Lake cluster versions – CM 7.4.0, Pre-Check: Data Lake Cluster.

Cloud 73

Cloud Data Lake Cloud Storage Metadata 73

Cloudera

MARCH 31, 2021

With the addition of Google Cloud, we deliver on our vision of providing a hybrid and multi-cloud architecture to support our customer’s analytics needs regardless of deployment platform. . You could then use an existing pipeline to run analytics on the prepared data in BigQuery. .

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Google Cloud Cloud Amazon Web Services Cloud Storage 94

Cloudera

JANUARY 21, 2021

Of high value to existing customers, Cloudera’s Data Warehouse service has a unique, separated architecture. . Separate storage. Cloudera’s Data Warehouse service allows raw data to be stored in the cloud storage of your choice (S3, ADLSg2). Proprietary file formats mean no one else is invited in!

IT 94

IT Data Lake Data Warehouse Cloud Storage 94

Data Engineering Podcast

SEPTEMBER 22, 2019

Summary Object storage is quickly becoming the unifying layer for data intensive applications and analytics. Modern, cloud oriented data warehouses and data lakes both rely on the durability and ease of use that it provides. How do you approach project governance and sustainability?

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AWS Google Cloud Cloud Storage Data Lake 100

Cloudera

JUNE 5, 2023

On May 3, 2023, Cloudera kicked off a contest called “Best in Flow” for NiFi developers to compete to build the best data pipelines. This blog is to congratulate our winner and review the top submissions. RK built some simple flows to pull streaming data into Google Cloud Storage and Snowflake.

Google Cloud 77

Google Cloud Cloud Storage Data Lake Data Pipeline 77

Cloudera

SEPTEMBER 15, 2020

Cloudera Data Platform 7.2.1 introduces fine-grained authorization for access to Azure Data Lake Storage using Apache Ranger policies. Cloudera and Microsoft have been working together closely on this integration, which greatly simplifies the security administration of access to ADLS-Gen2 cloud storage.

Accessible 79

Accessible Accessibility Cloud Cloud Storage 79

Cloudera

SEPTEMBER 15, 2022

Mark: The first element in the process is the link between the source data and the entry point into the data platform. At Ramsey International (RI), we refer to that layer in the architecture as the foundation, but others call it a staging area, raw zone, or even a source data lake.

Data Lake 83

Data Lake Analytics Application Cloud Storage Architecture 83

Data Engineering Weekly

AUGUST 11, 2024

link] Uber: Enabling Security for Hadoop Data Lake on Google Cloud Storage Uber writes about securing a Hadoop-based data lake on Google Cloud Platform (GCP) by replacing HDFS with Google Cloud Storage (GCS) while maintaining existing security models like Kerberos-based authentication.

Data Engineering 57

Data Engineering Data Engineer Google Cloud Engineering 57

Cloudera

DECEMBER 14, 2017

alpha2 on the Cloudera Engineering blog, and 3.0.0 Improved support for cloud storage systems like S3 (with S3Guard ), Microsoft Azure Data Lake, and Aliyun OSS. appeared first on Cloudera Blog. The Apache Hadoop community recently released version 3.0.0 We covered earlier releases like 3.0.0-alpha1

Hadoop 43

Hadoop Cloud Storage Data Lake Software Engineer 43

Cloudera

FEBRUARY 16, 2022

Each workspace is associated with a collection of cloud resources. In the case of CDP Public Cloud, this includes virtual networking constructs and the data lake as provided by a combination of a Cloudera Shared Data Experience (SDX) and the underlying cloud storage.

Machine Learning 85

Machine Learning Algorithm Government Metadata 85

Cloudera

AUGUST 21, 2020

Data-in-motion is predominantly about streaming data so enterprises typically have two different ways or binary ways of looking at data. Stay tuned for Part II of our Q&A with Dinesh as we dive deeper into how live streaming data and technology is helping businesses within the financial service sector. .

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Banking Kafka Cloud Storage Government 62

Cloudera

OCTOBER 26, 2017

Organizations find they have much more agility with analytics in the cloud and can operate at a lower cost point than has been possible with legacy on-premises solutions. Generally, instances for transient clusters need only minimal local disk space, since data processing runs directly on the data in the cloud storage.

Cloud 43

Cloud BI Cloud Storage Business Intelligence 43

AltexSoft

FEBRUARY 11, 2023

Data architecture is the organization and design of how data is collected, transformed, integrated, stored, and used by a company. If you are not familiar with the above-mentioned concepts, we suggest you to follow the links above to learn more about each of them in our blog posts.

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Data Architect Certification Generalist Big Data 87

Data Science Blog: Data Engineering

DECEMBER 23, 2022

Das Profil des Data Engineers: Big Data High-Tech Auch wenn Data Engineering von Hochschulen und Fortbildungsanbietern gerade noch etwas stiefmütterlich behandelt werden, werden der Einsatz und das daraus resultierende Anforderungsprofil eines Data Engineers am Markt recht eindeutig skizziert.

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Data Engineering Data Engineer Engineering Business Intelligence 52

ProjectPro

JANUARY 24, 2023

Tired of relentlessly searching for the most effective and powerful data warehousing solutions on the internet? This blog is your comprehensive guide to Google BigQuery, its architecture, and a beginner-friendly tutorial on how to use Google BigQuery for your data warehousing activities. Search no more! Did you know ?

Bytes 52

Bytes Google Cloud Data Warehouse Cloud Storage 52

Knowledge Hut

SEPTEMBER 21, 2023

Storage Services Azure Blob Storage, Azure Files, Azure Tables, Azure Queues, and Azure Data Lake Cloud SQL, Cloud Spanner, BigTable, Cloud Storage, and BigQuery 4. Azure vs. Google Cloud: Market Position Among the major players in cloud platforms are Microsoft Azure and Google Cloud Platform.

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Cloud Google Cloud Cloud Computing Cloud Storage 52

Edureka

FEBRUARY 9, 2023

This position requires knowledge of Microsoft Azure services such as Azure Data Factory, Azure Stream Analytics, Azure Databricks, Azure Cosmos DB, and Azure Storage. To store various types of data, various methods are used. Conclusion So this was all about the Azure data engineer skills.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Mining 52

Edureka

OCTOBER 10, 2024

Another element that can be identified in both services is the copy operation, with the help of which data can be transferred between different systems and formats. This activity is rather critical of migrating data, extending cloud and on-premises deployments, and getting data ready for analytics.

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MongoDB NoSQL Metadata Datasets 40

ProjectPro

DECEMBER 7, 2021

Data pipelines are a significant part of the big data domain, and every professional working or willing to work in this field must have extensive knowledge of them. Generally, data pipelines are created to store data in a data warehouse or data lake or provide information directly to the machine learning model development.

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Data Pipeline Architecture Kafka AWS 52

Knowledge Hut

JULY 6, 2023

IT Professionals looking to work in the cloud domain are expected to have a sound understanding of Azure tools as well as development and monitoring tools. This blog walks you through the top Azure Monitoring and Development that every SRE and DevOps engineer must know.

Amazon Web Services 52

Amazon Web Services Data Lake Java SQL 52

Knowledge Hut

APRIL 25, 2023

Fivetran Fivetran is a popular cloud-based data integration platform that simplifies the process of data engineering by automating data pipeline creation, management, and maintenance. Cloud Composer can integrate with other GCP services like BigQuery, Cloud Storage, and Cloud Dataflow.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

ProjectPro

AUGUST 24, 2021

Data professionals who work with raw data like data engineers, data analysts, machine learning scientists , and machine learning engineers also play a crucial role in any data science project. And, out of these professions, this blog will discuss the data engineering job role.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

ProjectPro

JANUARY 19, 2022

Planning to land a successful job as an Azure Data Engineer? Read this blog till the end to learn more about the roles and responsibilities, necessary skillsets, average salaries, and various important certifications that will help you build a successful career as an Azure Data Engineer. The final step is to publish your work.

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Data Engineering Data Engineer Engineering Data Storage 52

ProjectPro

FEBRUARY 16, 2023

of data engineer job postings on Indeed? If you are still wondering whether or why you need to master SQL for data engineering, read this blog to take a deep dive into the world of SQL for data engineering and how it can take your data engineering skills to the next level.

Data Engineering 52

Data Engineering Data Engineer SQL Engineering 52

Monte Carlo

JULY 19, 2023

In this article, we’ll present you with the Five Layer Data Stack—a model for platform development consisting of five critical tools that will not only allow you to maximize impact but empower you to grow with the needs of your organization. Before you can model the data for your stakeholders, you need a place to collect and store it.

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Building Business Intelligence Cloud Storage BI 96

Monte Carlo

JANUARY 27, 2024

Those tools include: Table of Contents Cloud storage and compute Data transformation Business Intelligence (BI) Data observability Data orchestration The most important part? Cloud storage and compute Whether you’re stacking data tools or pancakes, you always build from the bottom up.

Building 52

Building Business Intelligence Cloud Storage BI 52

Data Engineering Weekly

JANUARY 24, 2025

The world of data management is undergoing a rapid transformation. The rise of cloud storage, coupled with the increasing demand for real-time analytics, has led to the emergence of the Data Lakehouse. This paradigm combines the flexibility of data lakes with the performance and reliability of data warehouses.

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Architecture Metadata Data Ingestion Data Lake 76