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applies_to:
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stack:
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serverless:
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elasticsearch: all
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products:
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- id: elasticsearch
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- id: cloud-serverless
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navigation_title: Get started
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---
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# Get started
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# Get started with {{es}}
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## Core implementation decisions
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New to {{es}}? Start building a search experience by setting up your first deployment, refining your search goals, and adding data.
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% TODO add diagram
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:::{note}
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If you're looking for an introduction to the {{stack}} or the {{es}} product, go to [](/get-started/index.md) or [](/manage-data/data-store.md).
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:::
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Building a search experience with {{es}} requires a number of fundamental implementation decisions:
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:::::{stepper}
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::::{step} Choose your deployment type
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1.[**Deployment**](/deploy-manage/index.md): Where will you run Elastic?
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1.[**Ingestion**](ingest-for-search.md): What tools will you use to get your content into {{es}}?
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1.[**Search approaches**](search-approaches.md): What search techniques and algorithms will you use to find relevant results?
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1.**Implementation tools**: How will you write queries and interact with {{es}}?
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- Which programming language client matches your application?
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- Which API endpoints and [query language(s)](querying-for-search.md) will you use to express your search logic?
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Elastic provides several self-managed or Elastic-managed options.
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For simplicity and speed, try out [{{es-serverless}}](/solutions/search/serverless-elasticsearch-get-started.md) or [run {{es}} locally](/solutions/search/run-elasticsearch-locally.md).
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Check out the full list of [deployment types](/deploy-manage/deploy.md#choosing-your-deployment-type) to learn more.
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::::
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Each decision builds on the previous ones, offering flexibility to mix and match approaches based on your needs.
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::::{step} Identify your search goals
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::::{tip}
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Already have an {{es}} deployment? You can get started with our hands-on [quick start tutorials](api-quickstarts.md), or check out our [Python notebooks](https://github.com/elastic/elasticsearch-labs/tree/main/notebooks#readme).
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::::
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You can choose multiple [search approaches](search-approaches.md) (for example, full-text and semantic search) depending on your use case.
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Each approach affects your options for storing and querying your data.
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:::{tip}
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If you want to ingest your data first and transform or reindex it as needed later, you can skip to the next step.
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:::
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::::
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::::{{step}} Ingest your data
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To learn about adding data, go to [](/solutions/search/ingest-for-search.md).
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For a broader overview of ingestion options, go to [](/manage-data/ingest.md).
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If you're planning to perform vector or semantic [AI-powered search](/solutions/search/ai-search/ai-search.md), the approach that requires the least configuration involves adding `semantic_text` fields when you ingest your data.
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Try it out with [](/solutions/search/get-started/semantic-search.md).
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:::{tip}
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If you're not ready to add your own data, you can use [sample data](/manage-data/ingest/sample-data.md) or create small data sets when you follow the [quickstarts](/solutions/search/get-started/quickstarts.md).
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:::
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::::
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::::{{step}} Get started with your use case
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Not sure where to start exploring or which features may be relevant for you?
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View our [quickstarts](/solutions/search/get-started/quickstarts.md), which help you complete a core task so you can get up and running.
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For example, to learn about using clients to integrate with {{es}} in your application, check out [](/solutions/search/get-started/keyword-search-python.md).
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