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, Configure the required ports to enable connectivity from CDH to CDP Public Cloud (see docs for details).

Cloud 69

Cloud Data Lake Cloud Storage Metadata 69

Data Engineering Podcast

JUNE 12, 2022

Acryl Data provides DataHub as an easy to consume SaaS product which has been adopted by several companies. Signup for the SaaS product at dataengineeringpodcast.com/acryl RudderStack helps you build a customer data platform on your warehouse or data lake. What are the mechanisms that you use for categorizing data assets?

Unstructured Data 130

Unstructured Data MongoDB Scala MySQL 130

Cloudera

JUNE 5, 2023

RK built some simple flows to pull streaming data into Google Cloud Storage and Snowflake. Many developers use DataFlow to filter/enrich streams and ingest into cloud data lakes and warehouses where the ability to process and route anywhere makes DataFlow very effective.

Google Cloud 75

Google Cloud Cloud Storage Data Lake Data Pipeline 75

Cloudyard

AUGUST 22, 2023

With this feature, you can efficiently manage, govern, and analyze your data irrespective of its storage location, ensuring optimal data management. This feature significantly contributes to enhancing your data management capabilities within the Snowflake ecosystem. Zero egress fees mean zero vendor lock-in.”

Bytes 52

Bytes Data Lake Cloud Storage Cloud 52

Ascend.io

AUGUST 31, 2023

Secondly , the rise of data lakes that catalyzed the transition from ELT to ELT and paved the way for niche paradigms such as Reverse ETL and Zero-ETL. Still, these methods have been overshadowed by EtLT — the predominant approach reshaping today’s data landscape.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

Towards Data Science

APRIL 26, 2023

An open-source implementation of a Data Lake with DuckDB and AWS Lambdas A duck in the cloud. Photo by László Glatz on Unsplash In this post we will show how to build a simple end-to-end application in the cloud on a serverless infrastructure. The idea is to start from a Data Lake where our data are stored.

Engineering 70

Engineering Data Lake AWS BI 70

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 80

Data Lake Analytics Application Cloud Storage Architecture 80

Knowledge Hut

SEPTEMBER 26, 2023

Key connectivity features include: Data Ingestion: Databricks supports data ingestion from a variety of sources, including data lakes, databases, streaming platforms, and cloud storage. This flexibility allows organizations to ingest data from virtually anywhere.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

WeCloudData

OCTOBER 19, 2021

Conclusion WeCloudData helped a client build a flexible data pipeline to address the needs from multiple business units requiring different sets, views and timelines of job market data.

Consulting 52

Consulting Raw Data Data Lake Data Pipeline 52

WeCloudData

OCTOBER 19, 2021

Conclusion WeCloudData helped a client build a flexible data pipeline to address the needs from multiple business units requiring different sets, views and timelines of job market data.

Consulting 52

Consulting Raw Data Data Lake Data Pipeline 52

DareData

JANUARY 30, 2023

Data lakes: These are large-scale data storage systems that are designed to store and process large amounts of raw, unstructured data. Examples of technologies able to aggregate data in data lake format include Amazon S3 or Azure Data Lake. Stanford's Relational Databases and SQL.

Data Engineering 52

Data Engineering Data Engineer NoSQL 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.

Building 96

Building Business Intelligence Cloud Storage BI 96

Snowflake

FEBRUARY 23, 2023

Snowflake’s solution to ingesting very large healthcare pricing transparency data files. In the above solution approach, the pricing transparency JSON file is hosted in a cloud storage bucket and is referenced through an external stage on Snowflake.

Healthcare 52

Healthcare Hospitality Insurance Cloud Storage 52

Towards Data Science

DECEMBER 15, 2023

Even the best of us sometimes demonize the parts of our organization whose primary goals are in the privacy and security area and conflict with our wishes to splash around in the data lake. In reality, data scientists are not always the heroes and IT and security teams are not the villains. You’re using the data, of course!

Machine Learning 93

Machine Learning Data Science Data Security Data Storage 93

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 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?

AWS 100

AWS Google Cloud Cloud Storage Data Lake 100

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 75

Accessible Accessibility Cloud Cloud Storage 75

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: Storage Services Azure and Google Cloud both provide similar storage services in general.

Cloud 52

Cloud Google Cloud Cloud Computing Cloud Storage 52

Towards Data Science

JULY 21, 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.

Building 83

Building Business Intelligence BI Cloud Storage 83

ProjectPro

OCTOBER 6, 2021

Also, Cloud Endpoints are used, which help speed up the development, making smoother API calls for mobile app development. Data Lake using Google Cloud Platform What is a Data Lake? Data Lake is a centralized area or repository for data storage.

Google Cloud 52

Google Cloud Project Data Lake Healthcare 52

Monte Carlo

AUGUST 19, 2023

With companies moving their data platforms to the cloud, the emergence of cloud-native solutions ( data warehouse vs data lake or even a data lakehouse ) have taken over the market, offering more accessible and affordable options for storing data relative to many on-premises solutions.

Building 52

Building BI Data Lake Data Governance 52

Knowledge Hut

JULY 6, 2023

It is a built-in Massively parallel processing (MPP) data lake house to handle all your infrastructure observability and security needs. It is a free standalone application that makes working with Azure Storage data on Windows, macOS, and Linux effortlessly.

Amazon Web Services 52

Amazon Web Services Data Lake Java SQL 52

Ascend.io

MAY 18, 2023

The growing complexity drove a proliferation of software and data innovations, which in turn demanded highly trained data engineers to build code-based data pipelines that ensured data quality, consistency, and stability. Because data pipelines were coded from scratch, they started breaking down under the complexity.

Data Warehouse 52

Data Warehouse Data Pipeline Data Lake Data Engineering 52

Rockset

MAY 24, 2021

The most popular data storage layers for a serverless stack include: Amazon S3: Amazon Simple Storage Service is offered through AWS as a scalable infrastructure solution. Azure Data Lake: Microsoft's analytics platform and serverless data lake is offered through the company's public cloud, Azure.

Database 40

Database Google Cloud AWS Cloud Storage 40

Cloudera

DECEMBER 14, 2017

Improved support for cloud storage systems like S3 (with S3Guard ), Microsoft Azure Data Lake, and Aliyun OSS. YARN Timeline Service v2, which improves the scalability, reliability, and usability of the existing Timeline Service. See the Apache Hadoop 3.0.0 documentation for a full rundown of the changes.

Hadoop 43

Hadoop Cloud Storage Data Lake Software Engineer 43

Rockset

AUGUST 25, 2021

With this release , Rockset users have the capability to continuously aggregate and transform data at the time of ingest, using SQL, from any data source (data streams, databases and data lakes). This is a first in the industry and frees users from managing slow, expensive ETL pipelines for their streaming data.

Aggregated Data 52

Aggregated Data Data Lake Hadoop SQL 52

Rockset

AUGUST 4, 2021

Without performant data ingestion, you run the risk of querying outdated values and returning irrelevant analytics. Snowflake provides a couple of ways to load data. The first, bulk loading, loads data from files in cloud storage or a local machine. Then it stages them into a Snowflake cloud storage location.

Data Ingestion 40

Data Ingestion Cloud Storage Data Warehouse Data Lake 40

ProjectPro

JANUARY 24, 2023

BigQuery enables users to store data in tables, allowing them to quickly and easily access their data. It supports structured and unstructured data, allowing users to work with various formats. BigQuery also supports many data sources, including Google Cloud Storage, Google Drive, and Sheets.

Bytes 52

Bytes Google Cloud Data Warehouse Datasets 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

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.

Data Engineering 52

Data Engineering Data Engineer Engineering Business Intelligence 52

ProjectPro

DECEMBER 7, 2021

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. Keeping data in data warehouses or data lakes helps companies centralize the data for several data-driven initiatives.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

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 83

Machine Learning Algorithm Government Metadata 83

Edureka

FEBRUARY 9, 2023

To store various types of data, various methods are used. It is preferable to understand when to use a data lake versus a data warehouse to create data solutions for an organisation. Companies collect massive amounts of data, making automation essential for dealing with such massive amounts of data.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Mining 52

Cloudera

SEPTEMBER 10, 2021

Shared Data Experience ( SDX ) on Cloudera Data Platform ( CDP ) enables centralized data access control and audit for workloads in the Enterprise Data Cloud. The public cloud (CDP-PC) editions default to using cloud storage (S3 for AWS, ADLS-gen2 for Azure).

Cloud Storage 81

Cloud Storage Accessible Accessibility Cloud 81

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.

Banking 60

Banking Kafka Cloud Storage Government 60

AltexSoft

FEBRUARY 11, 2023

Data architecture is the organization and design of how data is collected, transformed, integrated, stored, and used by a company. machine learning and deep learning models; and business intelligence tools.

Data Architect 87

Data Architect Certification Generalist Big Data 87

ProjectPro

JANUARY 19, 2022

ETL (extract, transform, and load) techniques move data from databases and other systems into a single hub, such as a data warehouse. Different methods are used to store different types of data. It is better to know when to employ a data lake vs. a data warehouse to create data solutions for an organization.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52