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As your tools and edge cases grow, you deserve an extensible and open ELT solution that eliminates the time you spend on building and maintaining data pipelines
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- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
- Automated workflow with our Airflow, Dagster and Prefect integration
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- Airbyte Cloud: Have it hosted by us, with all the security you need (SOC2, ISO, GDPR, HIPAA Conduit).
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FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
A fully managed data warehouse service in the Amazon Web Services (AWS) cloud, Amazon Redshift is designed for storage and analysis of large-scale datasets. Redshift allows businesses to scale from a few hundred gigabytes to more than a petabyte (a million gigabytes), and utilizes ML techniques to analyze queries, offering businesses new insights from their data. Users can query and combine exabytes of data using standard SQL, and easily save their query results to their S3 data lake.
Typesense is an open-source, typo-tolerant search engine optimized for an instant (typically sub-50ms) search-like-up-type experience and developer productivity. If you've heard of Elasticsearch or Algolia, a good way to think about Typesense is that it's an open source alternative to Algolia, with some key issues fixed and an easy-to-use battery-powered alternative to Elasticsearch.It works like a CDN, but for Search. Deploy nodes around the world, closest to your users, to provide them an ultra-fast search experience.
Amazon Redshift provides access to a wide range of data related to the Redshift cluster, including:
1. Cluster metadata: Information about the cluster, such as its configuration, status, and performance metrics.
2. Query execution data: Details about queries executed on the cluster, including query text, execution time, and resource usage.
3. Cluster events: Notifications about events that occur on the cluster, such as node failures or cluster scaling.
4. Cluster snapshots: Point-in-time backups of the cluster, including metadata and data files.
5. Cluster security: Information about the cluster's security configuration, including user accounts, permissions, and encryption settings.
6. Cluster logs: Detailed logs of cluster activity, including system events, query execution, and error messages.
7. Cluster performance metrics: Metrics related to the cluster's performance, such as CPU usage, disk I/O, and network traffic.
Overall, Redshift's API provides a comprehensive set of data that can be used to monitor and optimize the performance of Redshift clusters, as well as to troubleshoot issues and manage security.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
