Blendata Enterprise is a hybrid data lakehouse platform that enables businesses to ingest, manage, analyze, and utilize both structured and unstructured data on both on-premises and in cloud environments. Powered by optimized de facto standard technologies like Apache Spark and Delta Lake. It can out-of-the-box connect seamlessly and integrate with diverse types of data sources including databases, cloud platforms, flat files, and more. Managing, securing, and governing all data in the single lakehouse technology, while being able to process data with ANSI & SparkSQL to perform data warehouse tasks, doing data engineer and data scientist jobs for advanced analytics workloads through high-code Python (Pyspark), Scala/Java with notebook interface, or even low-code data preparation with drag and drop GUI for business users. All of the jobs and tasks can be automated with low-code workflow management to scheduling the complex data & AI pipeline. With all of these capabilities, businesses don’t have to worry about the complexity of diverse tool sets, cost, and time incentives, while easily enabling data-driven strategy through big data and AI in no time with a reasonable cost.

Highlight Features

Unified and Simplified Platform, Powered by Best in Class Technologies:

Unify all big data & advanced analytics capabilities into one platform from data integration, data management and security, processing and analytics, to data utilization, Simplify all features into a single interface with low-code capability, All of these Lakehouse features powered by best-in-class technologies like Apache Spark and Delta Lake.

Managed Diverse Data Sources Transformed into Data Lakehouse:

Integrate data from various sources (including databases, files, logs, cloud, and enterprise databases), such as *Oracle, MySQL, Amazon S3, etc., effortlessly through built-in connectors with multiple advanced techniques such as Changed data capture, all without coding.

Managing Data with Enterprise Grade Security and Governance:

Offers enterprise-grade security and governance with powerful features like user/role-based access control, data masking and hashing for PII data, Data encryption with external KMS supported, and even granular column or row-level security

Writing Pyspark, R, or Scala through Notebook Interface:

Provide a familiar notebook interface for data engineers, data scientists, and ML developers, supporting popular programming languages like Python, R, Scala, and SQL with Apache Spark-based library.

SQL Analytics:

Offering a versatile data user interface like SQL editor with support for ANSI SQL and Spark SQL, allowing analysts and engineers to manage SQL warehouse tasks like correlations, aggregation, data exploration, and job scheduling.

Drag and Drop Data Preparation:

Easily link, filter, and create datasets from diverse sources using drag-and-drop, without coding. Suitable for super-users and non-technical persons.

Low-Code Workflow Management:

Automating efficient data pipeline operation, from ingestion, SQL script, and Notebook, to ML/AI utilization. With low-code workflow orchestration, users can easily schedule all the workloads with ease without the need to write the complex Python DAG or any script which comes with jobs monitoring and management console as well., This empowers organizations to perform complicated operations and automation tasks with minimal effort.

Built-in Dashboards and Visualization:

Easily create custom charts and visualizations from analyzed data, featuring filtering, drill-down options, and secure sharing within your organization via dashboard-style projects.

Connect with Any 3rd Parties Application with an Open Standard API:

All data and features can be connected through standard REST API or ODBC/JDBC (Spark). Supported all external 3rd parties applications like BI tools such as Tableau.

No Vendor Lock-In:

With open technologies that we chose like Apache Spark as our main engine, or big data file formats like Parquet and Delta Lake. It's both powerful and easy to develop, integrate, or migrate data and workloads to other platforms with minimal effort. This stands in contrast to proprietary technologies that restrict customer data and workloads within their ecosystem.