Best Data Lake Solutions for Elasticsearch

Find and compare the best Data Lake solutions for Elasticsearch in 2025

Use the comparison tool below to compare the top Data Lake solutions for Elasticsearch on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    Sprinkle Reviews

    Sprinkle

    Sprinkle Data

    $499 per month
    In today's fast-paced business environment, companies must quickly adjust to the constantly shifting demands and preferences of their customers. Sprinkle provides an agile analytics platform designed to manage these expectations effortlessly. Our mission in founding Sprinkle was to simplify the entire data analytics process for organizations, eliminating the hassle of integrating data from multiple sources, adapting to changing schemas, and overseeing complex pipelines. We have developed a user-friendly platform that allows individuals across all levels of an organization to explore and analyze data without needing technical expertise. Drawing on our extensive experience with data analytics in collaboration with industry leaders such as Flipkart, Inmobi, and Yahoo, we understand the importance of having dedicated teams of data scientists, business analysts, and engineers who are capable of generating valuable insights and reports. Many organizations, however, face challenges in achieving straightforward self-service reporting and effective data exploration. Recognizing this gap, we created a solution that enables all businesses to harness the power of their data effectively, ensuring they remain competitive in a data-driven world. Thus, our platform aims to empower organizations of all sizes to make informed decisions based on real-time data insights.
  • 2
    Kylo Reviews
    Kylo serves as an open-source platform designed for effective management of enterprise-level data lakes, facilitating self-service data ingestion and preparation while also incorporating robust metadata management, governance, security, and best practices derived from Think Big's extensive experience with over 150 big data implementation projects. It allows users to perform self-service data ingestion complemented by features for data cleansing, validation, and automatic profiling. Users can manipulate data effortlessly using visual SQL and an interactive transformation interface that is easy to navigate. The platform enables users to search and explore both data and metadata, examine data lineage, and access profiling statistics. Additionally, it provides tools to monitor the health of data feeds and services within the data lake, allowing users to track service level agreements (SLAs) and address performance issues effectively. Users can also create batch or streaming pipeline templates using Apache NiFi and register them with Kylo, thereby empowering self-service capabilities. Despite organizations investing substantial engineering resources to transfer data into Hadoop, they often face challenges in maintaining governance and ensuring data quality, but Kylo significantly eases the data ingestion process by allowing data owners to take control through its intuitive guided user interface. This innovative approach not only enhances operational efficiency but also fosters a culture of data ownership within organizations.
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