O'Reilly Media

SEPTEMBER 15, 2021

Cloud certifications, specifically in AWS and Microsoft Azure, were most strongly associated with salary increases. As we’ll see later, cloud certifications (specifically in AWS and Microsoft Azure) were the most popular and appeared to have the largest effect on salaries. Many respondents acquired certifications. What about Kafka?

AI 145

AI AI Azure AWS 145

Analytics Vidhya

MARCH 8, 2023

The Biggest Data Science Blogathon is now live! Knowledge is power. Sharing knowledge is the key to unlocking that power.”― Martin Uzochukwu Ugwu Analytics Vidhya is back with the largest data-sharing knowledge competition- The Data Science Blogathon.

Data Science 345

Data Science Analytics Analytics Apache Hadoop 345

phData

NOVEMBER 8, 2024

Cost Efficiency and Scalability Open Table Formats are designed to work with cloud storage solutions like Amazon S3, Google Cloud Storage, and Azure Blob Storage, enabling cost-effective and scalable storage solutions. Amazon S3, Azure Data Lake, or Google Cloud Storage).

Data Lakes 52

Data Lakes Data Warehouse Database Azure 52

Data Science Dojo

MAY 24, 2024

Java is also widely used in big data technologies, supported by powerful Java-based tools like Apache Hadoop and Spark, which are essential for data processing in AI. Big Data Technologies With the growth of data-driven technologies, AI engineers must be proficient in big data platforms like Hadoop, Spark, and NoSQL databases.

Deep Learning 195

Deep Learning Machine Learning Machine Learning Deep Learning 195

Analytics Vidhya

JANUARY 8, 2023

Hey, are you the data science geek who spends hours coding, learning a new language, or just exploring new avenues of data science? If all of these describe you, then this Blogathon announcement is for you! Analytics Vidhya is back with its 28th Edition of blogathon, a place where you can share your knowledge about […].

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Analytics Vidhya

NOVEMBER 7, 2022

Hello, fellow data science enthusiasts, did you miss imparting your knowledge in the previous blogathon due to a time crunch? Well, it’s okay because we are back with another blogathon where you can share your wisdom on numerous data science topics and connect with the community of fellow enthusiasts.

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Data Science Analytics Analytics Hadoop 306

Precisely

MARCH 9, 2023

Hadoop, Snowflake, Databricks and other products have rapidly gained adoption. We will also address some of the key distinctions between platforms like Hadoop and Snowflake, which have emerged as valuable tools in the quest to process and analyze ever larger volumes of structured, semi-structured, and unstructured data.

Data Warehouse 59

Data Warehouse Data Lakes Hadoop Big Data 59

phData

MARCH 7, 2023

#gg.eventhandler.hdfs.finalizeAction=delete #TODO: Edit snowflake storage integration to access Azure Blob Storage. share/hadoop/common/*:hadoop-3.2.1/share/hadoop/common/lib/*:hadoop-3.2.1/share/hadoop/hdfs/*:hadoop-3.2.1/share/hadoop/hdfs/lib/*:hadoop-3.2.1/etc/hadoop/:hadoop-3.2.1/share/hadoop/tools/lib/* gg.classpath=./snowflake-jdbc-3.13.7.jar:hadoop-3.2.1/share/hadoop/common/*:hadoop-3.2.1/share/hadoop/common/lib/*:hadoop-3.2.1/share/hadoop/hdfs/*:hadoop-3.2.1/share/ha

Hadoop 59

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Pickl AI

DECEMBER 25, 2024

Big data platforms such as Apache Hadoop and Spark help handle massive datasets efficiently. They must also stay updated on tools such as TensorFlow, Hadoop, and cloud-based platforms like AWS or Azure. Programming languages like Python and R are commonly used for data manipulation, visualization, and statistical modeling.

Data Science 52

Data Science Analytics Analytics Data Scientist 52

Smart Data Collective

APRIL 24, 2023

Google’s Hadoop allowed for unlimited data storage on inexpensive servers, which we now call the Cloud. Searching for a topic on a search engine can provide us with a vast amount of information in seconds. Deighton studies how this evolution came to be. Innovations in the early 20th century changed how data could be used.

Big Data 119

Big Data Big Data Hadoop Azure 119

ODSC - Open Data Science

MAY 31, 2023

Commonly used technologies for data storage are the Hadoop Distributed File System (HDFS), Amazon S3, Google Cloud Storage (GCS), or Azure Blob Storage, as well as tools like Apache Hive, Apache Spark, and TensorFlow for data processing and analytics.

Data Lakes 131

Data Lakes Machine Learning Machine Learning Apache Kafka 131

ODSC - Open Data Science

JUNE 7, 2023

Microsoft’s Azure Data Lake The Azure Data Lake is considered to be a top-tier service in the data storage market. Amazon Web Services Similar to Azure, Amazon Simple Storage Service is an object storage service offering scalability, data availability, security, and performance.

Data Lakes 52

Data Lakes Azure Data Warehouse Hadoop 52

Pickl AI

NOVEMBER 4, 2024

Among these tools, Apache Hadoop, Apache Spark, and Apache Kafka stand out for their unique capabilities and widespread usage. Apache Hadoop Hadoop is a powerful framework that enables distributed storage and processing of large data sets across clusters of computers.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

DataRobot Blog

OCTOBER 3, 2017

Many announcements at Strata centered on product integrations, with vendors closing the loop and turning tools into solutions, most notably: A Paxata-HDInsight solution demo, where Paxata showcased the general availability of its Adaptive Information Platform for Microsoft Azure. Alation and Paxata announced their product integration.

Data Lakes 52

Data Lakes Azure Data Pipeline Hadoop 52

Smart Data Collective

NOVEMBER 8, 2020

Spark outperforms old parallel systems such as Hadoop, as it is written using Scala and helps interface with other programming languages and other tools such as Dask. Popular cloud platforms include the Microsoft Azure, Google Cloud Platform, and Amazon Web Services. Data processing is often done in batches.

Data Engineer 104

Data Engineer Data Engineering Data Engineering Data Engineering 104

Data Science Dojo

JULY 3, 2024

Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud. Cloud Computing : Utilizing cloud services for data storage and processing, often covering platforms such as AWS, Azure, and Google Cloud.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

Smart Data Collective

AUGUST 29, 2019

With courses that cover areas from Microsoft’s Azure platform to Hadoop, EDX has a course for almost every big data specialty. They work with some of the industry’s biggest players, like Microsoft, to help produce detailed and engaging courses that can be taken from anywhere.

Big Data 85

Big Data Big Data Big Data Analytics Big Data Analytics 85

Pickl AI

JANUARY 13, 2025

Key Skills Experience with cloud platforms (AWS, Azure). Hadoop , Apache Spark ) is beneficial for handling large datasets effectively. Cloud Computing Skills Familiarize yourself with cloud platforms like AWS , Google Cloud , or Microsoft Azure to manage infrastructure and deploy AI models efficiently.

AI 52

AI AI Machine Learning Machine Learning 52

Pickl AI

NOVEMBER 5, 2024

Its popularity stems from its user-friendly interface and seamless integration with widely used Microsoft applications like Excel and Azure, making it highly accessible for organisations already using Microsoft products. Tableau supports integrations with third-party tools, including Salesforce, Hadoop, and Google Analytics.

Power BI 52

Power BI Tableau Business Intelligence Business Intelligence 52

Pickl AI

JUNE 7, 2024

Key Features Out-of-the-Box Connectors: Includes connectors for databases like Hadoop, CRM systems, XML, JSON, and more. Hadoop Hadoop is an open-source framework designed for processing and storing big data across clusters of computer servers. Read Further: Azure Data Engineer Jobs. How to drop a database in SQL server?

ETL 40

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Pickl AI

JULY 25, 2023

With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently. Big Data Technologies: Hadoop, Spark, etc. Big Data Processing: Apache Hadoop, Apache Spark, etc.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Pickl AI

DECEMBER 2, 2024

Processing frameworks like Hadoop enable efficient data analysis across clusters. Cloud Storage: Services like Amazon S3, Google Cloud Storage, and Microsoft Azure Blob Storage provide scalable storage solutions that can accommodate massive datasets with ease. Data lakes and cloud storage provide scalable solutions for large datasets.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Pickl AI

NOVEMBER 25, 2024

Processing frameworks like Hadoop enable efficient data analysis across clusters. Cloud Storage: Services like Amazon S3, Google Cloud Storage, and Microsoft Azure Blob Storage provide scalable storage solutions that can accommodate massive datasets with ease. Data lakes and cloud storage provide scalable solutions for large datasets.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Mlearning.ai

MAY 23, 2023

Check out this course to build your skillset in Seaborn —  [link] Big Data Technologies Familiarity with big data technologies like Apache Hadoop, Apache Spark, or distributed computing frameworks is becoming increasingly important as the volume and complexity of data continue to grow.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Mlearning.ai

JANUARY 28, 2023

Spark: Spark is a popular platform used for big data processing in the Hadoop ecosystem. Using a cloud provider such as Google Cloud Platform, Amazon AWS, Azure Cloud, or IBM SoftLayer 2. Deploying a machine learning library in the cloud can be difficult.

Machine Learning 52

Machine Learning Machine Learning Data Science ML 52

Mlearning.ai

MARCH 15, 2023

Experience with cloud platforms like; AWS, AZURE, etc. Knowledge of big data platforms like; Hadoop and Apache Spark. Experience with machine learning frameworks for supervised and unsupervised learning. Experience with visualization tools like; Tableau and Power BI.

Data Science 52

Data Science Analytics Analytics Data Analyst 52

DagsHub

DECEMBER 11, 2023

LakeFS Most big data storage solutions such as Azure, Google cloud storage, and Amazon S3 have good performance, cost-effective, and have good connectivity with other tooling. Such a server is not provided by every Git hosting service and in some cases will require either setting it up or switching to a different Git provider.

Machine Learning 98

Machine Learning Machine Learning Data Lakes Database 98

Pickl AI

NOVEMBER 28, 2024

Cloud platforms like AWS , Google Cloud Platform (GCP), and Microsoft Azure provide managed services for Machine Learning, offering tools for model training, storage, and inference at scale. Big Data Tools Integration Big data tools like Apache Spark and Hadoop are vital for managing and processing massive datasets.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

IBM Journey to AI blog

JANUARY 18, 2023

This is an architecture that’s well suited for the cloud since AWS S3 or Azure DLS2 can provide the requisite storage. It can include technologies that range from Oracle, Teradata and Apache Hadoop to Snowflake on Azure, RedShift on AWS or MS SQL in the on-premises data center, to name just a few. Yet, the overlap is evident.

Data Lakes 52

Data Lakes Data Warehouse Azure Apache Hadoop 52

Pickl AI

MARCH 11, 2025

Cloud platforms like AWS and Azure support Big Data tools, reducing costs and improving scalability. Companies like Amazon Web Services (AWS) and Microsoft Azure provide this service. It supports Big Data tools like Hadoop and Spark, allowing businesses to scale analytics operations efficiently. Google App Engine is an example.

Cloud Computing 52

Cloud Computing Big Data Big Data Big Data Analytics 52

DagsHub

OCTOBER 23, 2024

Popular data lake solutions include Amazon S3 , Azure Data Lake , and Hadoop. Apache Hadoop Apache Hadoop is an open-source framework that supports the distributed processing of large datasets across clusters of computers. Data Processing Tools These tools are essential for handling large volumes of unstructured data.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Pickl AI

DECEMBER 4, 2024

Gain Experience with Big Data Technologies With the rise of Big Data, familiarity with technologies like Hadoop and Spark is essential. Familiarise yourself with cloud platforms like AWS, Google Cloud Platform , or Microsoft Azure for storing and processing large datasets. Additionally, familiarity with cloud platforms (e.g.,

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Pickl AI

APRIL 2, 2024

Hadoop/Spark: Frameworks for distributed storage and processing of big data. Cloud Platforms (AWS, Azure, Google Cloud): Infrastructure for scalable and cost-effective data storage and analysis. SQL (Structured Query Language): Language for managing and querying relational databases.

Data Science 52

Data Science Machine Learning Machine Learning Predictive Analytics 52

The MLOps Blog

JANUARY 23, 2023

It supports most major cloud providers, such as AWS, GCP, and Azure. LakeFS is fully compatible with many ecosystems of data engineering tools such as AWS, Azure, Spark, Databrick, MlFlow, Hadoop and others. The remote repository can be on the same computer, or it can be on the cloud.

ML 52

ML ML Data Lakes Machine Learning 52

Heartbeat

JULY 31, 2023

Comet also integrates with popular data storage and processing tools like Amazon S3, Google Cloud Storage, and Hadoop. Comet also works with popular cloud platforms like AWS, GCP, and Azure, making it easy to deploy models to the cloud with just a few clicks.

Machine Learning 52

Machine Learning Machine Learning Deep Learning Deep Learning 52

ODSC - Open Data Science

JANUARY 7, 2025

Hadoop, though less common in new projects, is still crucial for batch processing and distributed storage in large-scale environments. Cloud Services Most major companies are using either Amazon Web Services (AWS) or Microsoft Azure, so excelling in one or the other will help any aspiring data scientist.

Data Scientist 40

Data Scientist Data Science Machine Learning Machine Learning 40

The MLOps Blog

MAY 31, 2023

To store Image data, Cloud storage like Amazon S3 and GCP buckets, Azure Blob Storage are some of the best options, whereas one might want to utilize Hadoop + Hive or BigQuery to store clickstream and other forms of text and tabular data. One might want to utilize an off-the-shelf ML Ops Platform to maintain different versions of data.

ML 59

ML ML Algorithm Machine Learning 59