Altair Monarch vs. Apify vs. AWS Glue

The product is especially useful when you have real-time and/or time series data to analyze. If you have more mundane, simpler requirements, other products might do the job you need for less money (there are even some decent open source visualization tools you can find.) I know the product is very widely used in capital markets applications to monitor and analyze risk and price and volume changes; if you're working in that area, I don't think there's a better tool to use.

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Apify is great when I need to collect data from public websites and get the export in a structured format, such as product catalogs, contacts, and location details, and if that data must be exported on a regular basis, the same actor can be reused again and again. However, when I need just a one-off task or information, Apify is less useful, as writing a script directly is much faster and cheaper. The platform is also less useful, when the website or provider where I need data in a structure format, has already some sort of an API or MCP connection available, then it makes sense just using the API I need, rather then a web scraper.

One of AWS Glue's most notable features that aid in the creation and transformation of data is its data catalog. Support, scheduling, and the automation of the data schema recognition make it superior to its competitors aside from that. It also integrates perfectly with other AWS tools. The main restriction may be integrated with systems outside of the AWS environment. It functions flawlessly with the current AWS services but not with other goods. Another potential restriction that comes to mind is that glue operates on a spark, which means the engineer needs to be conversant in the language.

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• Creating a basic model to extract data from a report is very easy.
• Advanced features like Calculated Fields and External Lookups allow you to augment the raw data.
• You can create a "project" to automate the data extraction. Combined with Datapump (a separate DW app), you can fully automate the process once the raw report is generated.

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• They feature a massive actor library, so you don't have to build everything from scratch or maintain it.
• Scheduling + web hooks makes automating data collection super easy.
• Easy to scale from small tests to much bigger scraping jobs and fairly cheap to scale.

• It is extremely fast, easy, and self-intuitive. Though it is a suite of services, it requires pretty less time to get control over it.
• As it is a managed service, one need not take care of a lot of underlying details. The identification of data schema, code generation, customization, and orchestration of the different job components allows the developers to focus on the core business problem without worrying about infrastructure issues.
• It is a pay-as-you-go service. So, there is no need to provide any capacity in advance. So, it makes scheduling much easier.

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• Recently, we had some major sticker-shock when we wanted to upgrade Data Pump. It is an exceptional product, but when the price jumped from $6,000 to over $60,000, it was impossible to get the funds approved internally for the upgrade.
• We also paid for yearly maintenance contracts which included Professional Services, but rarely found those services beneficial. However, we did receive all software upgrades for Datapump as part of the contract which we found to be very beneficial. However, with the new pricing, that is not longer the case.

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• I think monetization part needs some improvement. Sometimes it stops working.
• Second issue is that when you run actor locally on your device using Apify run command, it runs fine and does not show errors. However, when you run on Apify platform, it starts showing error that this python based library is not supported etc. I think, error logging needs to be improved.
• Issue support is very slow. If you create an issue it takes a lot of time that somebody reply.
• Automated testing also needs to be improved. Testers run simple tests and if extra keys are used or something else, test is failed.

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• In-Stream schema registries feature people can not use this more efficiently
• in Connections feature they can add more connectors as well
• The crucial problem with AWS Glue is that it only works with AWS.

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Even though we do not utilize it on a daily basis, I do hope my current company renews it's license. If not, I intend to purchase myself.

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It's the best because you can just load in a limited amount of money and limit your financial exposure to using a tool like this. so renewing isn't a big consideration... i know that i'm going to need the tool in the future and i'll load it with money accordingly when im ready.

Extremely easy when connected via MCP with Claude Co-Work. I do not really understand the Apify website itself. It's a bit confusing, but when I use Claude as an operator and translator, it is absolutely a game-changer. Whenever I have an issue, I ask Claude to connect to Apify and ask what the best way for me to do things is. Should I use the Google Maps scraper in this area or this area? Which one has the highest chance of giving me the best results that I need? Claude's code can completely simplify the whole process, which makes the whole thing a lot easier for me to make faster and better decisions. I do not know what I would do now without Apify. My business cannot even be a business without it.

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While easy to set up and manage monitoring for large datasets, its complexity can be a barrier for new users. Integration with AWS Ecosystem, Managed Monitoring, Dashboards and monitoring tools for AWS Glue are generally easy to set up and maintain, Automated Data Pipelines. Automates data pipeline creation, making it efficient for certain data integration

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We never needed to contact support directly because the platform was always clear and easy to use. The documentation is thorough, there are plenty of how-to guides, and YouTube.

Amazon responds in good time once the ticket has been generated but needs to generate tickets frequent because very few sample codes are available, and it's not cover all the scenarios.

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1. Strategy for Anti-Blocking & Proxies 2. Cost Optimization (Compute Units) 3. Data Management & Storage

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Datawatch is very good value of money compared to QlikView; QlikView is really more of a BI tool and has a lot of functions that I didn't need. Datawatch is very strong in the real-time area where Tableau, Panorama, and Qlik don't do very well. If you need to set up a visual monitoring dashboard, Datawatch is the best product I've seen for that. if you want to do a lot of in depth statistical analysis of large databases, Tableau is probably a good option.

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I use Oxylabs and ScraperAPI as paid backup tools; I also have backup parsers on my own VPS, set up to run on a timer. They solve the problem of retrieving data but do not help to present it as a product or find a buyer. I chose Apify because it turns a parser into something you can sell, and the proxies come as part of the infrastructure. Running your own infrastructure is cheaper—until you factor in the hours spent maintaining it.

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AWS Glue is a fully managed ETL service that automates many ETL tasks, making it easier to set AWS Glue simplifies ETL through a visual interface and automated code generation.

• Data Pump reduces complexity of report solutions by offering a standardized approach for organizing and scheduling
• 97% service level for past 5 years for all of our jobs going through Data Pump

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• Apify has delivered great ROI from the sheer time saved by not having to do even short-ish tasks manually.
• By saving us from repetitive data entry, we can focus on much bigger things while Apify runs in the background.
• The risk-free actor pricing structure lets us test and deploy new automated pipelines with zero upfront commitment.

• We are using GLUE for our ETL purpose. it’s ease with other our AWS services makes our ROI, 100% ROI.
• One missing piece was compatibility with other data source for which we found a work around and made our data source as S3 only, so our dependencies on other data source is also reducing

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