Monte Carlo

APRIL 24, 2023

By accommodating various data types, reducing preprocessing overhead, and offering scalability, data lakes have become an essential component of modern data platforms , particularly those serving streaming or machine learning use cases. See our post: Data Lakes vs. Data Warehouses.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

Towards Data Science

MARCH 5, 2024

BigQuery basics and understanding costs BigQuery is not just a tool but a package of scalable compute and storage technologies, with fast network, everything managed by Google. At its core, BigQuery is a serverless Data Warehouse for analytical purposes and built-in features like Machine Learning ( BigQuery ML ).

Bytes 69

Bytes Google Cloud Cloud Storage Utilities 69

phData: Data Engineering

AUGUST 4, 2023

Within Snowflake, data can either be stored locally or accessed from other cloud storage systems. What are the Different Storage Layers Available in Snowflake? In Snowflake, there are three different storage layers available, Database, Stage, and Cloud Storage.

Cloud Storage 52

Cloud Storage Google Cloud Amazon Web Services Data Storage 52

ProjectPro

JANUARY 24, 2023

Sounds exciting to learn more about Google BigQuery, read on! 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. These benefits compel businesses to adopt cloud data warehousing and take their success to the next level.

Bytes 52

Bytes Google Cloud Data Warehouse Datasets 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. What are Data Engineering Tools?

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

Ascend.io

JUNE 22, 2023

Next, build out connections to your sources and pipelines that deliver data to MotherDuck. In the example below, we connected to a MySQL database, Snowflake Data Warehouse and Google Cloud Storage (blob storage). You have tons of other options available in Ascend’s connection catalog!

NoSQL 52

NoSQL Google Cloud MySQL Data Pipeline 52

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. Ein Data Scientist, der Deep Learning verstanden hat, kann sein Wissen zügig sowohl mit TensorFlow als auch mit PyTorch anwenden.

Data Engineering 52

Data Engineering Data Engineer Engineering Business Intelligence 52

Striim

FEBRUARY 10, 2023

Quickly turn data into actionable insights that help Macy’s deliver quality digital customer experiences and improve operational efficiencies. Macy’s migrated its on-premise inventory and order data to Google Cloud storage to reach its objectives.

Data Analytics 52

Data Analytics Telecommunication Insurance Google Cloud 52

ProjectPro

SEPTEMBER 6, 2021

So, are you ready to explore the differences between two cloud giants, AWS vs. google cloud? It developed and optimized everything from cloud storage, computing, IaaS, and PaaS. And that is one big reason it is the market leader and dominates other cloud technologies aggressively. Let’s get started!

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AWS Amazon Web Services Google Cloud Cloud Storage 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?

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AWS Google Cloud Cloud Storage Data Lake 100

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. Read More: What is ETL?

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

Cloudera

FEBRUARY 7, 2019

Enabling this transformation is the HDP platform, along with SAS Viya on Google Cloud , which has delivered machine learning models and personalization at scale. This has become a core component of the fraud management framework for the business, and has helped solidify its role as the trusted keeper of sensitive data.

Utilities 65

Utilities Big Data Google Cloud Data Analytics 65

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.

Scala 64

Scala Data Lake BI Google Cloud 64

ProjectPro

OCTOBER 6, 2021

Flexera’s State of Cloud report highlighted that 41% of the survey respondents showed the most interest in using Google Cloud Platform for their future cloud computing projects. Google Cloud Platform is an online vendor of multiple cloud services which can be used publicly.

Google Cloud 52

Google Cloud Project Data Lake Healthcare 52

Knowledge Hut

NOVEMBER 16, 2023

Microsoft Azure is one such public cloud computing platform that provides a range of cloud services for computing, storing, and networking. There are some renowned cloud players like Amazon Web Services, Google Cloud, IBM Watson, etc., How Azure and Data Science are Shaping the Future?

Data Science 52

Data Science Technology Programming Language Cloud Computing 52

AltexSoft

MAY 12, 2023

There are several widely used unstructured data storage solutions such as data lakes (e.g., Amazon S3, Google Cloud Storage, Microsoft Azure Blob Storage), NoSQL databases (e.g., MongoDB, Cassandra), and big data processing frameworks (e.g., Hadoop, Apache Spark).

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

Hevo

MAY 8, 2023

The exponential rate of data generation in every modern business from various SaaS applications, Marketing Channels, etc. has compelled them to move from On-premise databases to Cloud-Based Data Warehouses.

Data Warehouse 52

Data Warehouse Machine Learning Cloud Database 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.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Knowledge Hut

JUNE 26, 2023

The Structured Streaming API offered by Spark makes it possible for data to be processed in real-time in mini-batches, which in turn offers low-latency processing capabilities. The processed data are uploaded to Google Cloud Storage, where they are then subjected to transformation with the assistance of dbt.

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

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

AltexSoft

JULY 29, 2022

It lets you run MapReduce and Spark jobs on data kept in Google Cloud Storage (instead of HDFS); or. Oracle Big Data Service , offering customers a fully-managed Hadoop environment in the cloud. Snowflake: an evolving ecosystem for all types of data. Google Cloud Platform: a relative of Apache Hadoop.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

AltexSoft

OCTOBER 21, 2022

popular SQL and NoSQL database management systems including Oracle, SQL Server, Postgres, MySQL, MongoDB, Cassandra, and more; cloud storage services — Amazon S3, Azure Blob, and Google Cloud Storage; message brokers such as ActiveMQ, IBM MQ, and RabbitMQ; Big Data processing systems like Hadoop ; and.

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Kafka Hadoop ETL Tools Big Data 93

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

ProjectPro

JULY 30, 2021

50 Cloud Computing Interview Questions and Answers f0r 2023 Knowing how to answer the most commonly asked cloud computing questions can increase your chances of landing your dream cloud computing job roles. What are some popular use cases for cloud computing? Google Cloud Platform(GCP) Interview Questions and Answers 1.

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Cloud Computing Cloud Amazon Web Services AWS 40

ProjectPro

MAY 31, 2021

For such scenarios, data-driven integration becomes less comfortable, so you must prefer event-based data integration. This project will teach you how to design and implement an event-based data integration pipeline on the Google Cloud Platform by processing data using DataFlow.

Big Data 52

Big Data Coding Project Hadoop 52