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Timothy Spann.   πŸ‡ΊπŸ‡¦
Timothy Spann. πŸ‡ΊπŸ‡¦

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Populating Your Secure Cloud Data Estates

Populating Your Secure Cloud Data Estates

Hydrating Your Clean Cloud Data Lake

I am hard pressed to keep up with Data Store + Query terminology du jour. Was it Data Lake House? All these giant bodies of water mostly stored in buckets (S3)? I agree there are lots of nuances and many different query engines on top of those various means for storing that data. I don't think everytime we add a twist we need to add increasingly silly terms on top. Is it to confuse users? developers? data engineers? companies? executives? Perhaps if we change our data warehouse name again we can get them to buy the same thing again.

Clearly it can't be one size fit all for all this different things? I know a lot of companies of various types and sizes and most don't approach the size of the data that companies like Netflix and LinkedIn have. I really like their innovation, but often those projects get released and then wither in obscurity.

A few projects look really good:

For me, if I can do the basic CRUD operations that applications, reports, dashboards and queries require then it works for me. With Apache NiFi, Apache Kafka, Apache Spark and Apache Flink supporting a data store then it is should be good. The one thing I have to be wary of is that datastores like Apache Kudu, Apache HBase and HDFS have been around for a long time and have many of the production killing bugs flushed out of it, multiple company support and robust Open Source Apache communities around them. If a new project doesn't it won't survive, get traction or will just sit out there orphaned. Let's build on what we have and try not to have a million half supported projects that are often abandoned or of unknown status. Apache Parquet and Apache ORC have shown themselves as really solid and having engines like Apache Hive and Apache Impala to query them is really important. Apache Ozone is looking very interesting for when Object Stores are not available.

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