In terms of most valuable features, Zaloni has different components. One is its ingestion aspect where you can create a lot of ingestions based on the file levels or based on the time. You have listeners actually continuously listening on certain poles, and as the data arrives it will start picking up. That is the one use-case that we have used.

Another one is batch ingestion where you can set up your timer. At the set time, it will check whether the file is present, then it will start the data pipeline and pick up the jobs to trigger.

Another feature is that it is time-based. That means the advantage with Zaloni is you can have both set up - either on data arrival or schedule-based, or you can have both, mix and match.

Once the ingestion starts, it has the connectivity to the rest of the big data components. You can store the data on S3 or wherever you want. It has different connectors to connect to on-premise and cloud. We were using on AWS, so our primary storage was the S3 buckets for the information. It uses the Hive as well. Without big data components, it cannot work on its own. You need to have big data installed fast, and Zaloni works on top of it.

It has Bedrock architecture which is a component that actually manages these schedules and all other activities. Bedrock is another tool within Zaloni itself.

There is one more component called Metadata Management. It's called EMDM I guess, but I don't remember exactly what the component is called. It allows you to manage your metadata management. It's kind of a business metadata. You can go ahead and write whatever fields that describe the data, then it manages them.

The most valuable feature is the ability to map data and write workflows with logic inside them.

It is possible to see the lineage and metadata information, which is useful even though we have not yet used it extensively in my organization.