Solving your ETL problems,
One question at a time

MongoDB is still a very young platform to judge to life or death. Learn more.

Learn the pros and cons of building your own Python ETL versus using an automated ETL tool.

Before deciding on an ETL tool you need to define your data pipeline requirements. Learn more.

Event-driven ETL, also known as a data pipeline, differs from classic ETL in that they treat source data as a stream rather than pulling batches of data every (insert-interval-here). The first thing to consider is - do you need a data pipeline (event-driven ETL)?

Learn about the advantages Snowflake has over Amazon Redshift, including costs, database features, and maintenance.

Loading a CSV to Redshift is a pretty straightforward process, however some caveats do exist, especially when it comes to error-handling and keeping performance in mind. Learn more here.

There are a few ways to address this problem, and it mostly depends on what the requirements are: incremental dump and load, full dump & load, binlog replication, or a SaaS provider. Learn more.

ETL is the process of extracting data from a data store used for a certain task(s), and loading it to another data store to be used for some other task.

Striim is a real-time, streaming analytics and data integration platform. This allows analysis while data is “in-flight”.

Confluent is a data streaming platform based on Apache Kafka: a full-scale streaming platform, capable of not only publish-and-subscribe, but also the storage and processing of data within the stream.

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