Scraper API
ScrapingBee
Octoparse
Bright Data
Apify
Zyte
Scrapy
Oxylabs
Amazon EMR
Google BigQuery
Google Cloud Dataflow
Google Cloud Dataproc
Qubole
Snowflake
HortonWorks Data Platform
Databricks
ScraperAPI is a powerful and efficient web scraping API and tool designed to empower developers, data scientists, and businesses with reliable data extraction at scale. Our robust proxy API for web scraping simplifies web scraping, ensuring consistent access to vital web data without the frustration of IP bans or rate limits.
We take the complexity out of web scraping by handling the technical hurdles, including intelligent IP rotation, automatic CAPTCHA resolution, advanced parsing, and seamless JavaScript rendering. This allows you to focus on extracting valuable insights, making your web scraping projects more efficient and straightforward.
Scraper API
Amazon EMRAmazon EMR is recommended for data engineers, data scientists, and IT professionals who need to manage and process large datasets in a scalable, efficient, and cost-effective manner. It is especially suitable for businesses that are already using AWS services and want to leverage a tightly integrated ecosystem. Additionally, it is a good choice for organizations that require rapid and flexible data analysis capabilities provided by frameworks such as Hadoop, Spark, HBase, and Presto.
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We are using Scraper API more than 6 months. The product is very effective and we integrate it into our SaaS software.
Based on our record, Amazon EMR should be more popular than Scraper API. It has been mentiond 10 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Yeah, scraperapi.com also has a feature called "autoparse", and it converts some sites that it supports (e.g. Amazon) to JSON. Source: about 4 years ago
There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka. Source: over 3 years ago
I'm going to guess you want something like EMR. Which can take large data sets segment it across multiple executors and coalesce the data back into a final dataset. Source: about 4 years ago
This is exactly the kind of workload EMR was made for, you can even run it serverless nowadays. Athena might be a viable option as well. Source: about 4 years ago
Apache Spark is one of the most actively developed open-source projects in big data. The following code examples require that you have Spark set up and can execute Python code using the PySpark library. The examples also require that you have your data in Amazon S3 (Simple Storage Service). All this is set up on AWS EMR (Elastic MapReduce). - Source: dev.to / almost 5 years ago
Check out https://aws.amazon.com/emr/. Source: over 4 years ago
ScrapingBee - ScrapingBee is a Web Scraping API that handles proxies and Headless browser for you, so you can focus on extracting the data you want, and nothing else.
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
Octoparse - Octoparse provides easy web scraping for anyone. Our advanced web crawler, allows users to turn web pages into structured spreadsheets within clicks.
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
Bright Data - World's largest proxy service with a residential proxy network of 72M IPs worldwide and proxy management interface for zero coding.
Google Cloud Dataproc - Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost