elasticsearch Consulting & Support
Elasticsearch
NextBrick provides complete range of consulting and implementation services for Elastic product ecosystem includes:
NextBrick uses its expertise in Big Data analytics and Search to offer consulting services to help you reach your goal faster, following best practices that ensure your solution is efficient and scalable.
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Our clients
NextBrick’s Offerings in the Search space
- Browse and search, content spotlighting and relevancy ranking
- Unified information integration and contextual integration of structured and unstructured content
- Clustering, classification, entity extraction, aggregate structured and unstructured content
- Employee engagement dashboard and contextual employee information
- Customer service dashboard and improve CSR productivity
- BI on structured and unstructured data, sentiment analysis, control and executive dashboards
- Regulatory compliance to records management and e-discovery
- Relationship mining, semantic processing and contextual information integration
What We Do
Indexing Development
Make application stable with increased speed of indexing and removing latency of bringing data into elastic search. Create domain & problem specific optimized index creation.
Relevancy Tuning
Relevancy Tuning will help your users in getting desired results as fast as possible with fewer clicks or words. This will enable you in evaluating how good the current search is.
API Development
Provide fast, scalable, robust API endpoints, which connect UI to Elastic search queries. Close development of API endpoints and queries will help in avoid many integration bugs.
Performance Tuning
Performance Tuning will help in understanding the health of the machines, health of the cluster. Improving those will improve the overall experience of end users.
Elasticsearch Solutions
Indexing
- Data stored in Lucene Inverted Index format
- Easy to replicate the index from one node to another
- Advance concepts like Field cache helps in making the aggregation and sorting faster
- More flexibility in terms of managing the indices like creating indices based on dates, categories etc.
Schema & Mapping
- Elasticsearch is “schema-less” by nature - “Configure only when you really need to”
- Dynamic Mapping- Automatically apply generic mapping definitions to types which do not have mappings predefined.
- While indexing any “unseen” document, a mapping is dynamically created for it
- Dynamically elastic stores both versions of data
Relevancy
- Boosting the rank of document from query side as well as during indexing time
- Proximity for relevance-we can get the results by setting the proximity in our query (For ex: minimum_should_match = 30%).
- N-Gram Partial Matching functionality for getting the relevant results by providing the length
Percolator
- Used for alerting , monitoring and document filtering based on the queries
- The percolator and most of its features work in real-time, so once a percolate query is indexed it can immediately be used in the percolate API.
- Percolator can also help in auto tagging documents as they are getting indexed which might help in creating better navigation and user experience.
Sizing and Performance
- OOTB clustering is simple, two instances with same cluster name and in the same network will automatically connect.
- Support for AWS discovery if on AWS system.
- Can be configured to perform Unicast and multicast discovery.
Snapshot and Restore
- ES provides different ways to get backup of indices as FS, AWS, HDFS.
- The snapshot process is get executed without blocking index.
- The snapshot process is incremental.