Monte Carlo

APRIL 24, 2023

A notebook-based environment allows data engineers, data scientists, and analysts to work together seamlessly, streamlining data processing, model development, and deployment. Databricks also pioneered the modern data lakehouse architecture, which combines the best of data lakes and data warehouses.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

ProjectPro

JANUARY 24, 2023

Since its public release in 2011, BigQuery has been marketed as a unique analytics cloud data warehouse tool that requires no virtual machines or hardware resources. BigQuery is a highly scalable data warehouse platform with a built-in query engine offered by Google Cloud Platform. What is Google BigQuery Used for?

Bytes 52

Bytes Google Cloud Data Warehouse Datasets 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!

AWS 52

AWS Amazon Web Services Google Cloud Cloud Storage 52

Knowledge Hut

APRIL 25, 2023

Here, we'll take a look at the top data engineer tools in 2023 that are essential for data professionals to succeed in their roles. These tools include both open-source and commercial options, as well as offerings from major cloud providers like AWS, Azure, and Google Cloud. What are Data Engineering Tools?

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

dbt Developer Hub

NOVEMBER 22, 2022

Once your data warehouse is built out, the vast majority of your data will have come from other SaaS tools, internal databases, or customer data platforms (CDPs). Spreadsheets are the Swiss army knife of data processing. But there’s another unsung hero of the analytics engineering toolkit: the humble spreadsheet.

Data Warehouse 52

Data Warehouse ETL Tools Google Cloud Cloud Storage 52

Cloudera

SEPTEMBER 30, 2022

With DFF, users now have the choice of deploying NiFi flows not only as long-running auto scaling Kubernetes clusters but also as functions on cloud providers’ serverless compute services including AWS Lambda, Azure Functions, and Google Cloud Functions.

Google Cloud 72

Google Cloud AWS Cloud Cloud Storage 72

Monte Carlo

NOVEMBER 14, 2023

Let’s take a look at 11 other data orchestration tools challenging Airflow’s seat at the table. Prefect’s control panel also offers scheduling, automatic retries, and instant alerting, ensuring you always have a clear view of your data processes. First things first—what’s Airflow? Dagster Image courtesy of Dagster.

Pipeline-centric 40

Pipeline-centric Google Cloud Data Workflow Python 40

Knowledge Hut

JULY 24, 2023

In-memory Databases For applications that demand real-time data processing, in-memory databases are created. These databases use RAM-based data storage, which offers quicker access and response times than disk-based storage. Increased Data Security Data security is becoming a more significant issue as data expands.

Database 52

Database NoSQL Management Relational Database 52

Knowledge Hut

JUNE 26, 2023

If you want to break into the field of data engineering but don't yet have any expertise in the field, compiling a portfolio of data engineering projects may help. Data pipeline best practices should be shown in these initiatives. Source Code: Finnhub API with Kafka for Real-Time Financial Market Data Pipeline 3.

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

Knowledge Hut

JULY 24, 2023

Continue reading to learn about the myriad uses of cloud computing in manufacturing and how it can be used in the manufacturing sector. Solution Architect training courses will help you build competency in managing cloud storage and acquiring essential skills to seamlessly deploy, train, and manage workloads on cloud platforms.

Cloud Computing 52

Cloud Computing Manufacturing Cloud AWS 52

Knowledge Hut

NOVEMBER 18, 2023

On-device Machine Learning On-device machine learning refers to applications that are present on devices (web browsers and applications) rather than sending data to a server for data processing. These solutions can also help organizations reduce data transfer and as a result their cloud storage costs.

Machine Learning 52

Machine Learning Transportation Healthcare Medical 52

Ascend.io

AUGUST 31, 2023

There are a range of tools dedicated to just the extraction (“E”) function to land data in any type of data warehouse or data lake. Once in place, any transformations on the data are performed directly in the data lake on demand as different analytical tasks come up.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

Confluent

FEBRUARY 6, 2019

Managed model server in the public cloud like Google Cloud Machine Learning Engine: The cloud provider takes over the burden of availability and reliability. The data scientist “just” deploys its trained model, and production engineers can access it. Apache Kafka and KSQL for data scientists and data engineers.

Machine Learning 107

Machine Learning Python Kafka Java 107

ProjectPro

AUGUST 24, 2021

Source Code: Event Data Analysis using AWS ELK Stack 5) Data Ingestion This project involves data ingestion and processing pipeline with real-time streaming and batch loads on the Google cloud platform (GCP). The Yelp dataset, which is used for academic and research purposes, is processed here.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

U-Next

JULY 2, 2022

Introduction To Cloud Engineer Skills. The cloud computing model delivers computing resources on-demand – that is, through the Internet – such as data storage, compute power and data processing.

Cloud 40

Cloud Engineering Cloud Computing Amazon Web Services 40

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

Monte Carlo

AUGUST 25, 2023

While data lake vendors are constantly emerging to provide more managed services — like Databricks’ Delta Lake, Dremio, and even Snowflake — traditionally, data lakes have been created by combining various technologies. Storage can utilize S3, Google Cloud Storage, Microsoft Azure Blob Storage, or Hadoop HDFS.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

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

ProjectPro

MAY 18, 2016

Big data industry has made Hadoop as the cornerstone technology for large scale data processing but deploying and maintaining Hadoop clusters is not a cakewalk. Verizon- Offers Cloudera distribution on top of its cloud infrastructure. Altiscale- Offering a purpose-built Hadoop in the cloud.

Hadoop 40

Hadoop Google Cloud Cloud Computing Big Data 40

Monte Carlo

AUGUST 19, 2023

Recently, there’s been a lot of discussion around whether to go with open source or closed source solutions (the dialogue between Snowflake and Databricks’ marketing teams really brings this to light) when it comes to building your data platform.

Building 52

Building BI Data Lake Data Governance 52

Rockset

JUNE 7, 2021

This often leads to data being pulled in batches anywhere from large batches pulled once a day to lots of small batches pulled frequently. The rule of thumb is that if you are looking to build a real-time data processing system then the push approach should be used.

IT 40

IT Kafka Database MongoDB 40

AltexSoft

JULY 29, 2022

It relieves the MapReduce engine of scheduling tasks and decouples data processing from resource management. 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.

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.

Kafka 93

Kafka Hadoop ETL Tools Big Data 93

AltexSoft

MAY 12, 2023

Without a fixed schema, the data can vary in structure and organization. File systems, data lakes, and Big Data processing frameworks like Hadoop and Spark are often utilized for managing and analyzing unstructured data. These robust security measures ensure that data is always secure and private.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

AltexSoft

MARCH 30, 2023

Source: Databricks Delta Lake is an open-source, file-based storage layer that adds reliability and functionality to existing data lakes built on Amazon S3, Google Cloud Storage, Azure Data Lake Storage, Alibaba Cloud, HDFS ( Hadoop distributed file system), and others.

Scala 64

Scala Data Lake BI Google Cloud 64