Data Engineering
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Read White PaperBig Data Engineering for Machine Learning
How big data engines are used for exploring and preparing data, building pipelines, and delivering data sets to ML applications
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Read BlogThe Key to Building Data Pipelines for Machine Learning: Support for Multiple Engines
Which engines are most effective for each stage of the data engineering cycle
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Read ArticleStreamlining Operations of Machine Learning Models
Guest authors: Jerry Xu, Co-founder and CEO Datatron; Lekhni Randive, Product Manager, Datatron Qubole author: Jorge Villamariona, Sr. Product Marketing Manager, Qubole In today’s world,… The post...
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Read ArticleApache Sqoop 1.4.7 – 9 reasons why you need it
The sixth release of Apache Sqoop i.e. 1.4.7 is out! This is one of the most significant updates to the Sqoop platform. We give you… The post Apache Sqoop 1.4.7 – 9 reasons why you need it...
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Read Case StudyPredicting, Detecting, and Eliminating Online Threats: Malwarebytes
The cybersecurity company yields greater data-processing at lower costs, and realizes more powerful insights with Qubole.
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Read Case StudyNeustar Boosts Automation, Efficiency, and Savings with Qubole
The flexibility, APIs, and financial governance offered by Qubole enables Neustar to automate its solutions.
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Read FlipbookWorkload-Aware: Auto-Scaling A new paradigm for Big Data Workloads
Learn more about Workload-Aware-Auto-Scaling-- an alternative architectural approach to Auto-Scaling that is better suited for the Cloud and applications like Hadoop, Spark and Presto.
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Read ArticleMastering Data Governance on Cloud Data Lakes with Multiple Engines
Qubole data privacy and integrity experts cover how to maintain data integrity and privacy of data residing in data lakes using various open-source engines.
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Read Case StudyAgilOne: Machine Learning at Enterprise Scale
AgilOne runs a variety of workloads for querying data, running ML models, orchestrating ML workflows, and more on Qubole
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Watch WebinarData Engineering Pitfalls and How to Avoid Them
Simple, practical solutions for common challenges faced by data engineering teams
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Watch WebinarHow To Build Scalable Data Pipelines for Machine Learning
Common challenges faced by data engineers when building pipelines for ML and how to address them
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Read BlogWhat’s New with Airflow on Qubole? DAG Explorer and More
Use Apache Airflow to author workflows as directed acyclic graphs (DAGs) of tasks
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Read BlogETL Processes with AWS Data Pipeline And Qubole
How to facilitate event-based processing of long running ETL processes with AWS Data Pipeline and Qubole
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Read BlogAirflow on Anaconda: A Match Made in Heaven, Perfected by Qubole
How Airflow on Anaconda makes running machine learning pipelines and data science tasks seamless
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Read Report
Big Data Activation Report
The data on big data -- what engines are used most, for what, and which are the rising stars.
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Read ReportG2 Crowd Grid Report for Big Data Processing and Distribution | Fall 2019
Which vendors rank highest in customer satisfaction for big data processing
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Watch WebinarLeveraging Streaming and Batch Data Sets for ML Applications
Learn how to use Qubole to acquire and transform data sets for data science and analytics, make data sets available to different users, and fully leverage your data lake.
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Read Case StudyNauto Improves its Data Scientist Productivity, Accelerates Product Development
Nauto Improves its Data Scientist Prodcutivity, Accelerates Product Development
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Read Case StudyIbotta Builds a Self-Service Data Lake to Enable Business Growth
Ibotta cut costs thanks to Qubole’s autoscaling and downscaling capabilities, and the ability to isolate workloads to separate clusters.
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Read Case StudyPoshmark Experiences Hyper Growth and Uses Qubole to Create Value for the Poshmark Community
Qubole saved Poshmark up to one year to start transforming big data into creating value for its community
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