Towards Data Science

MARCH 5, 2024

In that case, queries are still processed using the BigQuery compute infrastructure but read data from GCS instead. Such external tables come with some disadvantages but in some cases it can be more cost efficient to have the data stored in GCS. Data can easily be uploaded and stored for low costs.

Bytes 72

Bytes Google Cloud Cloud Storage Utilities 72

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

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

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.

Bytes 52

Bytes Google Cloud Data Warehouse Datasets 52

ProjectPro

SEPTEMBER 6, 2021

Are you confused about choosing the best cloud platform for your next data engineering project ? AWS vs. GCP blog compares the two major cloud platforms to help you choose the best one. So, are you ready to explore the differences between two cloud giants, AWS vs. google cloud? Let’s get started!

AWS 52

AWS Amazon Web Services Google Cloud Cloud Storage 52

Cloudera

SEPTEMBER 30, 2022

Cloudera DataFlow for the Public Cloud (CDF-PC) is a cloud-native service for Apache NiFi within the Cloudera Data Platform (CDP). Since its initial release in 2021, CDF-PC has been helping customers solve their data distribution use cases that need high throughput and low latency requiring always-running clusters.

Google Cloud 73

Google Cloud AWS Cloud Cloud Storage 73

U-Next

JULY 2, 2022

Always wondered what the right skills to become an excellent cloud engineer are? 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

Monte Carlo

NOVEMBER 14, 2023

Similar to a traffic director for information, data orchestration tools gather data from various locations, organize it into a usable format, and then activate it for analysis and consumption. So, what makes a good data orchestration tool? 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

Get ready to discover fascinating insights, uncover mind-boggling facts, and explore the transformative potential of cutting-edge technologies like blockchain, cloud computing, and artificial intelligence. Disruptive Database Technologies All existing and upcoming businesses are adopting innovative ways of handling data.

Database 52

Database NoSQL Management Relational Database 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

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. There are several widely used unstructured data storage solutions such as data lakes (e.g.,

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

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: Use Stack Overflow Data for Analytic Purposes 4.

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

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. Delta Lake integrations.

Scala 64

Scala Data Lake BI Google Cloud 64

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

AltexSoft

JULY 29, 2022

It relieves the MapReduce engine of scheduling tasks and decouples data processing from resource management. They enable you to access the framework functionality in the cloud, typically charging fees per cluster/ per hour. It lets you run MapReduce and Spark jobs on data kept in Google Cloud Storage (instead of HDFS); or.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

Ascend.io

AUGUST 31, 2023

In the dynamic world of data, many professionals are still fixated on traditional patterns of data warehousing and ETL, even while their organizations are migrating to the cloud and adopting cloud-native data services. Central to this transformation are two shifts. Let’s take a closer look.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

Confluent

FEBRUARY 6, 2019

Let’s take a look at some of these options: Official standards like Open Neural Network Exchange (ONNX), Portable Format for Analytics (PFA) or Predictive Model Markup Language (PMML): A data scientist builds a model with Python. This approach supports different frameworks, products and cloud services.

Machine Learning 107

Machine Learning Python Kafka Java 107

ProjectPro

AUGUST 24, 2021

This is an end-to-end big data project for building a data engineering pipeline involving data extraction, data cleansing, data transformation, exploratory analysis , data visualization, data modeling, and data flow orchestration of event data on the cloud.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Monte Carlo

AUGUST 25, 2023

Modern companies are ingesting, storing, transforming, and leveraging more data to drive more decision-making than ever before. At the same time, 81% of IT leaders say their C-suite has mandated no additional spending or a reduction of cloud costs. And Snowflake continues to drive the data warehouse vs. data lake paradigm forward.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

AltexSoft

OCTOBER 21, 2022

A single cluster can span across multiple data centers and cloud facilities. This allows for easy horizontal scaling — just add new servers or data centers to your existing infrastructure to handle more amount of data. cloud data warehouses — for example, Snowflake , Google BigQuery, and Amazon Redshift.

Kafka 93

Kafka Hadoop ETL Tools Big Data 93

phData: Data Engineering

AUGUST 4, 2023

Data storage is a vital aspect of any Snowflake Data Cloud database. Within Snowflake, data can either be stored locally or accessed from other cloud storage systems. 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 19, 2023

It becomes especially important to have a data storage and processing layer when you start to deal with large amounts of data and are holding that data for a long period of time and need it to be readily available for analysis. Singer – An open source tool for moving data from a source to a destination.

Building 52

Building BI Data Lake Data Governance 52

ProjectPro

MAY 31, 2021

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. Work on this project to learn how to use the Snowflake architecture and create a data warehouse in the cloud to bring value to your business.

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. The demand for cost effective big data management is driving the growth of HDaaS market. Verizon- Offers Cloudera distribution on top of its cloud infrastructure.

Hadoop 40

Hadoop Google Cloud Cloud Computing Big Data 40

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