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OpenSearch in 2025: Far more than an Elasticsearch fork
Cloud Computing

OpenSearch in 2025: Far more than an Elasticsearch fork

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Actual-Time Analytics on Kinesis Occasion Streams Utilizing Rockset, Druid, Elasticsearch and Redshift
Big Data

Actual-Time Analytics on Kinesis Occasion Streams Utilizing Rockset, Druid, Elasticsearch and Redshift

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Updates, Inserts, Deletes: Avoiding pitfalls when indexing dynamic data in Elasticsearch? When dealing with mutable information in Elasticsearch, you should steer clear of Updates, Inserts, and Deletes that can lead to inconsistencies and performance issues.
Big Data

Updates, Inserts, Deletes: Avoiding pitfalls when indexing dynamic data in Elasticsearch? When dealing with mutable information in Elasticsearch, you should steer clear of Updates, Inserts, and Deletes that can lead to inconsistencies and performance issues.

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Amazon OpenSearch Service announces End-of-Sale (EOS) and End-of-Life (EOL) dates for Elasticsearch and OpenSearch variants
Big Data

Amazon OpenSearch Service announces End-of-Sale (EOS) and End-of-Life (EOL) dates for Elasticsearch and OpenSearch variants

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Migrating from Elasticsearch to Rockset for Real-time Analytics: A 5-Step Guide As you prepare to upgrade your real-time analytics capabilities, considering the right platform is crucial.
Big Data

Migrating from Elasticsearch to Rockset for Real-time Analytics: A 5-Step Guide As you prepare to upgrade your real-time analytics capabilities, considering the right platform is crucial.

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Faced with the daunting task of optimizing Elasticsearch performance at scale? Here are four straightforward strategies to help you resolve common efficiency challenges and keep your data retrieval humming: Avoid unnecessary queries by leveraging filters: By utilizing filters, you can significantly reduce the number of queries required to retrieve relevant data, thereby minimizing latency.
Big Data

Faced with the daunting task of optimizing Elasticsearch performance at scale? Here are four straightforward strategies to help you resolve common efficiency challenges and keep your data retrieval humming: Avoid unnecessary queries by leveraging filters: By utilizing filters, you can significantly reduce the number of queries required to retrieve relevant data, thereby minimizing latency.

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As data volumes continue to swell, optimizing database performance becomes increasingly crucial. One strategy is to offload search and analytics workloads from primary databases like DynamoDB, leveraging a scalable indexing solution like Elasticsearch.
Big Data

As data volumes continue to swell, optimizing database performance becomes increasingly crucial. One strategy is to offload search and analytics workloads from primary databases like DynamoDB, leveraging a scalable indexing solution like Elasticsearch.

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What’s the optimal way to streamline data ingestion for large-scale analytics? In pursuit of efficient data pipelines, we put Elasticsearch and Rockset head-to-head in a real-world test drive. Our goal? To gauge performance under heavy loads. Streaming Ingest Benchmark: The Setup We simulated an IoT sensor stream with 100k+ messages per second, each containing roughly 1KB of JSON payload. We chose Elasticsearch (v7.10) and Rockset (v0.13) as our test subjects. The Ingestion Showdown With identical configurations, we fired up both platforms to see which one could handle the influx better. **Elasticsearch** Peak CPU usage: 30% Peak memory usage: 15GB Average latency: 300ms **Rockset** Peak CPU usage: 20% Peak memory usage: 10GB Average latency: 150ms The Verdict? Rockset outperformed Elasticsearch by a significant margin, thanks to its optimized architecture for high-throughput ingest.
Big Data

What’s the optimal way to streamline data ingestion for large-scale analytics? In pursuit of efficient data pipelines, we put Elasticsearch and Rockset head-to-head in a real-world test drive. Our goal? To gauge performance under heavy loads. Streaming Ingest Benchmark: The Setup We simulated an IoT sensor stream with 100k+ messages per second, each containing roughly 1KB of JSON payload. We chose Elasticsearch (v7.10) and Rockset (v0.13) as our test subjects. The Ingestion Showdown With identical configurations, we fired up both platforms to see which one could handle the influx better. **Elasticsearch** Peak CPU usage: 30% Peak memory usage: 15GB Average latency: 300ms **Rockset** Peak CPU usage: 20% Peak memory usage: 10GB Average latency: 150ms The Verdict? Rockset outperformed Elasticsearch by a significant margin, thanks to its optimized architecture for high-throughput ingest.

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How do I leverage DynamoDB’s secondary indexes for optimal query performance? The answer lies in choosing the right indexing technique. DynamoDB offers two types of secondary indexes: Global Secondary Index (GSI) and Local Secondary Index (LSI). Both can be useful, but which one to choose depends on your use case. For a GSI, you specify a unique attribute pair that serves as the primary key for the index. This allows you to query data across multiple tables without having to physically join them, making it ideal for aggregate queries and large datasets. LSIs are designed for queries that filter or group by a specific column. You can create an LSI on a single table, which enables efficient sorting, filtering, and grouping based on that column. However, there are also alternatives like Elasticsearch and Rockset that integrate seamlessly with DynamoDB. If you anticipate complex searches or need to leverage your existing Elasticsearch skills, consider using the former. In conclusion, selecting the correct indexing technique depends on the query patterns and requirements of your application. By choosing the right combination of GSI, LSI, Elasticsearch, or Rockset, you can ensure optimal performance for your DynamoDB tables.
Big Data

How do I leverage DynamoDB’s secondary indexes for optimal query performance? The answer lies in choosing the right indexing technique. DynamoDB offers two types of secondary indexes: Global Secondary Index (GSI) and Local Secondary Index (LSI). Both can be useful, but which one to choose depends on your use case. For a GSI, you specify a unique attribute pair that serves as the primary key for the index. This allows you to query data across multiple tables without having to physically join them, making it ideal for aggregate queries and large datasets. LSIs are designed for queries that filter or group by a specific column. You can create an LSI on a single table, which enables efficient sorting, filtering, and grouping based on that column. However, there are also alternatives like Elasticsearch and Rockset that integrate seamlessly with DynamoDB. If you anticipate complex searches or need to leverage your existing Elasticsearch skills, consider using the former. In conclusion, selecting the correct indexing technique depends on the query patterns and requirements of your application. By choosing the right combination of GSI, LSI, Elasticsearch, or Rockset, you can ensure optimal performance for your DynamoDB tables.

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You can perform joins in Elasticsearch using its query language and a technique called “outer join”. This is achieved by combining multiple queries with the `bool` query.
Big Data

You can perform joins in Elasticsearch using its query language and a technique called “outer join”. This is achieved by combining multiple queries with the `bool` query.

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