Varada Reviews and Pricing 2024

Audience

Companies and IT departments interested in a solution to manage their data and improve performance

About Varada

Varada’s dynamic and adaptive big data indexing solution enables to balance performance and cost with zero data-ops. Varada’s unique big data indexing technology serves as a smart acceleration layer on your data lake, which remains the single source of truth, and runs in the customer cloud environment (VPC). Varada enables data teams to democratize data by operationalizing the entire data lake while ensuring interactive performance, without the need to move data, model or manually optimize. Our secret sauce is our ability to automatically and dynamically index relevant data, at the structure and granularity of the source. Varada enables any query to meet continuously evolving performance and concurrency requirements for users and analytics API calls, while keeping costs predictable and under control. The platform seamlessly chooses which queries to accelerate and which data to index. Varada elastically adjusts the cluster to meet demand and optimize cost and performance.

Upsolver

Upsolver makes it incredibly simple to build a governed data lake and to manage, integrate and prepare streaming data for analysis. Define pipelines using only SQL on auto-generated schema-on-read. Easy visual IDE to accelerate building pipelines. Add Upserts and Deletes to data lake tables. Blend streaming and large-scale batch data. Automated schema evolution and reprocessing from previous state. Automatic orchestration of pipelines (no DAGs). Fully-managed execution at scale. Strong consistency guarantee over object storage. Near-zero maintenance overhead for analytics-ready data. Built-in hygiene for data lake tables including columnar formats, partitioning, compaction and vacuuming. 100,000 events per second (billions daily) at low cost. Continuous lock-free compaction to avoid “small files” problem. Parquet-based tables for fast queries.

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Azure Data Lake Storage

Eliminate data silos with a single storage platform. Optimize costs with tiered storage and policy management. Authenticate data using Azure Active Directory (Azure AD) and role-based access control (RBAC). And help protect data with security features like encryption at rest and advanced threat protection. Highly secure with flexible mechanisms for protection across data access, encryption, and network-level control. Single storage platform for ingestion, processing, and visualization that supports the most common analytics frameworks. Cost optimization via independent scaling of storage and compute, lifecycle policy management, and object-level tiering. Meet any capacity requirements and manage data with ease, with the Azure global infrastructure. Run large-scale analytics queries at consistently high performance.

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Hydrolix

Hydrolix is a streaming data lake that combines decoupled storage, indexed search, and stream processing to deliver real-time query performance at terabyte-scale for a radically lower cost. CFOs love the 4x reduction in data retention costs. Product teams love 4x more data to work with. Spin up resources when you need them and scale to zero when you don’t. Fine-tune resource consumption and performance by workload to control costs. Imagine what you can build when you don’t have to sacrifice data because of budget. Ingest, enrich, and transform log data from multiple sources including Kafka, Kinesis, and HTTP. Return just the data you need, no matter how big your data is. Reduce latency and costs, eliminate timeouts, and brute force queries. Storage is decoupled from ingest and query, allowing each to independently scale to meet performance and budget targets. Hydrolix’s high-density compression (HDX) typically reduces 1TB of stored data to 55GB.

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Dremio

Dremio delivers lightning-fast queries and a self-service semantic layer directly on your data lake storage. No moving data to proprietary data warehouses, no cubes, no aggregation tables or extracts. Just flexibility and control for data architects, and self-service for data consumers. Dremio technologies like Data Reflections, Columnar Cloud Cache (C3) and Predictive Pipelining work alongside Apache Arrow to make queries on your data lake storage very, very fast. An abstraction layer enables IT to apply security and business meaning, while enabling analysts and data scientists to explore data and derive new virtual datasets. Dremio’s semantic layer is an integrated, searchable catalog that indexes all of your metadata, so business users can easily make sense of your data. Virtual datasets and spaces make up the semantic layer, and are all indexed and searchable.

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Integrations

API:

Yes, Varada offers API access

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Ratings/Reviews

Overall 0.0 / 5

ease 0.0 / 5

features 0.0 / 5

design 0.0 / 5

support 0.0 / 5

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