Data Science Blog: Data Engineering

DECEMBER 23, 2022

Noch konkreter wird der Bedarf an Datenbeschaffung und -aufbereitung in der Business Intelligence, denn diese benötigt für nachhaltiges Reporting feste Strukturen wie etwa ein Data Warehouse. Andere Arten von Datenbanken, sogenannte NoSQL -Datenbanken beruhen auf Dateiformaten, einer Spalten- oder einer Graphenorientiertheit.

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Data Engineering Data Engineer Engineering Business Intelligence 52

Knowledge Hut

APRIL 25, 2023

Data engineers add meaning to the data for companies, be it by designing infrastructure or developing algorithms. The practice requires them to use a mix of various programming languages, data warehouses, and tools. While they go about it - enter big data data engineer tools.

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Data Engineering Data Engineer Engineering Google Cloud 52

ProjectPro

AUGUST 11, 2021

“Data Lake vs Data Warehouse = Load First, Think Later vs Think First, Load Later” The terms data lake and data warehouse are frequently stumbled upon when it comes to storing large volumes of data. Data Warehouse Architecture What is a Data lake?

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Data Lake Data Warehouse Cloud Hadoop 52

Monte Carlo

AUGUST 25, 2023

Different vendors offering data warehouses, data lakes, and now data lakehouses all offer their own distinct advantages and disadvantages for data teams to consider. So let’s get to the bottom of the big question: what kind of data storage layer will provide the strongest foundation for your data platform?

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Data Lake Data Warehouse Unstructured Data Raw Data 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.

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Data Pipeline Architecture Kafka AWS 52

Data Engineering Podcast

FEBRUARY 18, 2024

Summary A data lakehouse is intended to combine the benefits of data lakes (cost effective, scalable storage and compute) and data warehouses (user friendly SQL interface). Data lakes are notoriously complex. Go to dataengineeringpodcast.com/dagster today to get started.

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Data Lake High Quality Data Data Warehouse Google Cloud 262

Rockset

AUGUST 4, 2021

Organizations that depend on data for their success and survival need robust, scalable data architecture, typically employing a data warehouse for analytics needs. Snowflake is often their cloud-native data warehouse of choice. Snowflake provides a couple of ways to load data.

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Data Ingestion Cloud Storage Data Warehouse Data Lake 40

U-Next

SEPTEMBER 7, 2022

The terms “ Data Warehouse ” and “ Data Lake ” may have confused you, and you have some questions. Essentially, this is the difference between a lake and a warehouse. On the other hand, a data warehouse contains historical data that has been cleaned and arranged. .

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Data Lake Data Warehouse Unstructured Data Amazon Web Services 52

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.

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Google Cloud Project Data Lake Healthcare 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.

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Database Google Cloud AWS Cloud Storage 40

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.

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Machine Learning Algorithm Government Metadata 84

ProjectPro

FEBRUARY 16, 2023

Businesses will be better able to make smart decisions and achieve a competitive advantage if they can successfully integrate data from various sources using SQL. If your database is cloud-based, using SQL to clean data is far more effective than scripting languages. But how does SQL play a vital role here?

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Data Engineering Data Engineer SQL Engineering 52

AltexSoft

FEBRUARY 11, 2023

It serves as a foundation for the entire data management strategy and consists of multiple components including data pipelines; , on-premises and cloud storage facilities – data lakes , data warehouses , data hubs ;, data streaming and Big Data analytics solutions ( Hadoop , Spark , Kafka , etc.);

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Data Architect Certification Generalist Big Data 87

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).

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Cloud Storage Accessible Accessibility Cloud 82

Knowledge Hut

JUNE 26, 2023

The incoming data would be analogous to an event that occurred when a person listened to music, navigated around the website, or authenticated themselves. The processing of the data would take place in real-time, and it would be saved to the data lake at regular intervals (every two minutes).

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Data Engineering Data Engineer Coding Project 98

Cloudera

JANUARY 21, 2021

While cloud-native, point-solution data warehouse services may serve your immediate business needs, there are dangers to the corporation as a whole when you do your own IT this way. Cloudera Data Warehouse (CDW) is here to save the day! CDP is Cloudera’s new hybrid cloud, multi-function data platform.

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IT Data Lake Data Warehouse Cloud Storage 93

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.

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Engineering Data Lake AWS BI 67

Knowledge Hut

SEPTEMBER 26, 2023

If you want a deep understanding of cloud computing, you can check out Cloud Computing online courses. Microsoft Azure's Azure Synapse, formerly known as Azure SQL Data Warehouse, is a complete analytics offering. This flexibility allows organizations to ingest data from virtually anywhere.

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Data Lake Database-centric Pipeline-centric Machine Learning 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.

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Data Engineering Data Engineer NoSQL Engineering 52

Ascend.io

MAY 18, 2023

The data stack consisted of databases and third-party solutions with GUI drag-and-drop interfaces, such as Informatica and SSIS, that essentially wrote the SQL code for you to extract, transform, and load (ETL) data from the transactional databases into data warehouses — while managing their data processing schedules and data mapping needs.

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Data Warehouse Data Pipeline Data Lake Data Engineering 52

Edureka

FEBRUARY 9, 2023

Data is moved from databases and other systems into a single hub, such as a data warehouse, using ETL (extract, transform, and load) techniques. To store various types of data, various methods are used. A data engineer should be familiar with popular Big Data tools and technologies such as Hadoop, MongoDB, and Kafka.

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Data Engineering Data Engineer Engineering Data Mining 52

ProjectPro

JANUARY 24, 2023

With the global cloud data warehousing market likely to be worth $10.42 billion by 2026, cloud data warehousing is now more critical than ever. Cloud data warehouses offer significant benefits to organizations, including faster real-time insights, higher scalability, and lower overhead expenses.

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Bytes Google Cloud Data Warehouse Datasets 52

ProjectPro

AUGUST 24, 2021

Data Warehousing: Data warehousing utilizes and builds a warehouse for storing data. A data engineer interacts with this warehouse almost on an everyday basis. Data Analytics: A data engineer works with different teams who will leverage that data for business solutions.

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Data Engineering Data Engineer Coding Project 52

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.

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Data Engineering Data Engineer Engineering Scala 52

ProjectPro

NOVEMBER 30, 2021

In extract-transform-load (ETL), data is obtained from multiple sources, transformed, and stored in a single data warehouse, with access to data analysts , data scientists , and business analysts for data visualization and statistical analysis model building, forecasting, etc.

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Process Data Pipeline Data Warehouse AWS 52

AltexSoft

JULY 29, 2022

a runtime environment (sandbox) for classic business intelligence (BI), advanced analysis of large volumes of data, predictive maintenance , and data discovery and exploration; a store for raw data; a tool for large-scale data integration ; and. a suitable technology to implement data lake architecture.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

Cloudera

OCTOBER 26, 2017

Check out Greg Rahn’s session, “ Rethinking data marts in the cloud: Common architectural patterns for analytics ” at the Strata Data Conference in Singapore, December 4-7, 2017, to learn how to architect analytic workloads in the cloud and the core elements of data governance.

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Cloud BI Cloud Storage Business Intelligence 43

AltexSoft

MAY 12, 2023

Unstructured data , on the other hand, is unpredictable and has no fixed schema, making it more challenging to analyze. Without a fixed schema, the data can vary in structure and organization. There are several widely used unstructured data storage solutions such as data lakes (e.g., Hadoop, Apache Spark).

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Unstructured Data NoSQL Hadoop Data Lake 59

AltexSoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.

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Scala Data Lake BI Google Cloud 64

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.

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Building BI Data Lake Data Governance 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.

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Building Business Intelligence Cloud Storage BI 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.

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Building Business Intelligence BI Cloud Storage 78