AWS Glue and Cribl are competitors in the data integration and log management space. AWS Glue holds the upper hand with its strong integration within AWS ecosystems and serverless scalability.
Features: AWS Glue offers an integrated data catalog, efficient ETL scheduling triggers, and seamless integration with AWS services. Its serverless architecture allows scalable solutions without infrastructure management. Cribl's standout features include real-time data transformation within the pipeline, simplified log collection from multiple sources, and flexible data routing capabilities.
Room for Improvement: AWS Glue could improve start-up times, support more programming languages like Java, and enhance database connectivity. Users also seek better documentation and monitoring. Cribl could enhance compatibility with legacy systems, improve versioning, and expand its documentation and knowledge base for user clarity.
Ease of Deployment and Customer Service: AWS Glue supports public and hybrid cloud deployments, backed by generally positive AWS support that could benefit from enhanced response times and consistent communication. Cribl offers on-premises and hybrid cloud deployments, with positive feedback on customer service, though documentation and user community engagement need improvement.
Pricing and ROI: AWS Glue's pay-as-you-go model offers scalability but can become costly with extensive use. Cribl provides more cost-effective pricing, particularly for large data volumes, offering competitive ROI, especially for companies managing large data arrays efficiently.
I advocate using Glue in such cases.
AWS's documentation is reliable, and careful reference often resolves missed upgrade details.
The community, including the engineering and sales teams, is available on Slack and is very supportive.
It is beneficial to upgrade jobs, and we conduct extensive testing in development before migrating to production.
It can easily handle data from one terabyte to 100 terabytes or more, scaling nicely with larger datasets.
As a managed service, it reduces management burdens.
Migrating jobs from version 3.0 to 4.0 can present compatibility issues.
With AWS, I gather data from multiple sources, clean it up, normalize it, de-duplicate it, and make it presentable.
A more user-friendly and simpler process would help speed up the deployment process.
Perhaps more flexibility in terms of metrics would be helpful.
Costing depends on resource usage, and cost optimization may involve redesigning jobs for flexibility.
The smallest cost for a project is around €700, while the largest can reach up to €7,000 based on the scale of the usage.
AWS charges based on runtime, which can be quite pricey.
For ETL, I feel the performance is excellent. If I create jobs in a standard way, the performance is great, and maintenance is also seamless.
AWS Glue also enhances job scheduling and orchestration capabilities, integrating with AWS Glue Studio for comprehensive data workflow management.
I think if I'm working with big data, common languages like Python work quite nicely, which is advantageous.
The community on Slack is excellent for solving questions and getting ideas.
AWS Glue is a serverless cloud data integration tool that facilitates the discovery, preparation, movement, and integration of data from multiple sources for machine learning (ML), analytics, and application development. The solution includes additional productivity and data ops tooling for running jobs, implementing business workflows, and authoring.
AWS Glue allows users to connect to more than 70 diverse data sources and manage data in a centralized data catalog. The solution facilitates visual creation, running, and monitoring of extract, transform, and load (ETL) pipelines to load data into users' data lakes. This Amazon product seamlessly integrates with other native applications of the brand and allows users to search and query cataloged data using Amazon EMR, Amazon Athena, and Amazon Redshift Spectrum.
The solution also utilizes application programming interface (API) operations to transform users' data, create runtime logs, store job logic, and create notifications for monitoring job runs. The console of AWS Glue connects all of these services into a managed application, facilitating the monitoring and operational processes. The solution also performs provisioning and management of the resources required to run users' workloads in order to minimize manual work time for organizations.
AWS Glue Features
AWS Glue groups its features into four categories - discover, prepare, integrate, and transform. Within those groups are the following features:
AWS Glue Benefits
AWS Glue offers a wide range of benefits for its users. These benefits include:
Reviews from Real Users
Mustapha A., a cloud data engineer at Jems Groupe, likes AWS Glue because it is a product that is great for serverless data transformations.
Liana I., CEO at Quark Technologies SRL, describes AWS Glue as a highly scalable, reliable, and beneficial pay-as-you-go pricing model.
Cribl optimizes log collection, data processing, and migration to Splunk Cloud, ensuring efficient data ingestion and management for improved operational efficiency.
Cribl offers seamless log collection directly from cloud sources, allowing users to visually extract necessary data and replay specific events for in-depth analysis. It provides robust management of events, parsing, and enrichment of data, along with effective log size reduction. Cribl is particularly beneficial for migrating enterprise logs, optimizing usage, and reducing costs while streamlining the transition between different log management tools.
What are Cribl's most important features?
What benefits and ROI should users look for?
Cribl is widely implemented in industries requiring extensive data management, such as technology and finance. Users leverage Cribl to handle log collection, processing, and migration efficiently, ensuring smooth operation and effective data analysis. It aids in managing temporary data storage during downtimes and better handling historical data, preventing data loss and allowing extended periods for viewing statistics and monitoring trends.
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