200X Acceleration at
1/10th of the cost
Zero
maintenance
No credit card
required
Zero coding
infrastructure
Multi-level
security
Simplify SnappyData integration in
4 simple steps
Create connections
between SnappyData and targets.
Prepare pipeline
between SnappyData and targets by selecting tables in bulk.
Create a workflow
and schedule it to kickstart the migration.
Share your data
with third-party platforms over API Hub
Why choose Lyftrondata for SnappyData Integration?
Simplicity
Build your SnappyData pipeline and experience unparalleled data performance with zero training.
Robust Security
Load your SnappyData data to targets with end-to-end encryption and security.
Accelerated ROI
Rely on the cost-effective environment to ensure your drive maximum ROI.
Customer's Metrics
Track the engagement of your customers across different channels like email, website, chat, and more.
Improved Productivity
Measure the performance of your team and highlight areas of improvement.
360-degree Customer View
Join different data touch points and deliver personalized customer experience.
Hassle-free SnappyData integration to the platforms of your choice
Migrate your SnappyData data to the leading cloud data warehouses, BI tools, databases or Machine Learning platforms without writing any code.
Your Cloud Data Warehouses
Your BI Tools
Your Database
Your Machine Learning Tools
Hear how Lyftrondata helped accelerate the data journey of our customers
FAQs
What is SnappyData?
SnappyData is an open-source, distributed, in-memory data platform that combines the processing capabilities of Apache Spark with the real-time analytics features of an in-memory database. It is designed to handle both transactional and analytical workloads in a single cluster, offering real-time analytics on fast-moving data while also supporting traditional SQL queries.
What are the features of SnappyData?
Real-Time Analytics:
Enables real-time stream processing and analytics, which is beneficial for use cases like fraud detection, monitoring, and recommendation engines.
Hybrid In-Memory Processing:
Combines in-memory database features with Apache Spark’s analytics engine, allowing for high-speed transactional and analytical queries on the same data set.
Integration with Apache Spark:
Fully integrates with Spark, enabling users to run Spark SQL, machine learning, and graph processing while benefiting from the low-latency performance of an in-memory database.
SQL Support:
Offers full support for ANSI SQL queries, making it easy to query and manipulate data using familiar SQL syntax.
What are the shortcomings of SnappyData?
Complex Setup and Configuration:
Steep Learning Curve: SnappyData combines features from both Apache Spark and an in-memory database, which can make setup and configuration more complex for users who are not familiar with both ecosystems.
Cluster Management: Managing a distributed cluster with SnappyData can be challenging, particularly when it comes to scaling, ensuring data consistency, and tuning performance.
Limited Community Support:
Smaller User Base: Compared to more mature platforms like Apache Spark or traditional relational databases, SnappyData has a smaller user base and community, which can limit the availability of resources, tutorials, and community-driven support.
Slower Bug Fixes: With fewer contributors, bug fixes and feature updates may be slower compared to larger open-source projects.
Lack of Advanced Database Features:
Missing Features: Compared to traditional databases, SnappyData may lack advanced database features such as fine-grained access control, stored procedures, and comprehensive ACID (Atomicity, Consistency, Isolation, Durability) compliance.
Transactional Limitations: Although it supports transactional workloads, SnappyData’s transactional guarantees may not be as strong as dedicated transactional databases for highly sensitive data environments.
Make smarter decisions and grow your sales with Lyftrondata SnappyData integration
