Semantic search with ELSER in Elasticsearch
Elastic Learned Sparse EncodeR(ELSER) is a retrieval model trained by Elastic that enables you to perform semantic search to retrieve more relevant search results.
- install ELSER v2: Only once (DevOPs will do for your)
- Create source index where you can insert all your documents
- Create target index
- Create ingestion pipeline
- Reindex process to create embeddings
- Ready to do semantic search using text expansion queries
I created this blog post on docker to demonstrate Linux-optimised ELSER v2. The Elasticsearch version is 8.11.1.
Kafka PySpark streaming example
The diagram shows that the Kafka producer reads from Wikimedia and writes to the Kafka topic. Then Kafka Spark consumer pulls the data from the Kafka topic and writes the steam batches to disk.
Terraform For each iteration
This is to explain Terraform for each looping technique. In this example, 3 buckets are created to demonstrate the looping idea.
In the first step, we will create the above 3 buckets starting from 0.
Spark to create a table in AWS Redshift
In this post, Spark reads the data from a CSV file to a DateFrame and saves that DataFrame as a Redshift table.
In addition to that, I’ve explained how to create a table in Postgres, use Jupyter magics and plot a diagram.
Spark Kafka Docker Configuration
This is the continuation of the Spark Streaming Basics. I explained the basic stream example, which runs only on one AWS Glue container. The stream producer was Netcat, and the sink was a text file. In this post, the stream producer is still Netcat, but the sink is Kafka. Both Kafka and Spark running on Docker containers.