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One-time and complex queries are two common scenarios in enterprise dataanalytics. Complex queries, on the other hand, refer to large-scale data processing and in-depth analysis based on petabyte-level data warehouses in massive data scenarios.
That’s a fair point, and it places emphasis on what is most important – what best practices should data teams employ to apply observability to dataanalytics. We see data observability as a component of DataOps. In our definition of data observability, we put the focus on the important goal of eliminating data errors.
OpenSearch Service automatically assigns primary shards and replica shards to separate data nodes. It’s not possible to increase the primary shard number of an existing index, meaning an index must be recreated if you want to increase the primary shard count. The source index can still be used for querying and processing the data.
Offers different query types , allowing to prioritize data freshness (Snapshot Query) or read performance (Read Optimized Query). Clustering data for better data colocation using z-ordering. Considerations Data skipping using metadata column stats has to be supported in the query engine (currently only in Apache Spark).
Running HBase on Amazon S3 has several added benefits, including lower costs, data durability, and easier scalability. And during HBase migration, you can export the snapshot files to S3 and use them for recovery. HBase provided by other cloud platforms doesn’t support snapshots.
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