Connectivity to Data Sources
Lyftrondata can connect to over 200 data source types like relational data sources, MPP, NoSQL, SaaS, CRM’s, or social media and supports data access technologies like ODBC, JDBC, and ADO.Net. Click on Connection to learn how to connect to a data source.
Click Data Sources to see all source connectors.
Emulation of SQL Server
Lyftrondata's emulation of Microsoft SQL Server allows any client supporting connectivity to SQL Server to connect to Lyftrondata.
Emulation delivers compatibility with the following SQL Server features:
Metadata model exposed by SQL Server, including metadata catalog, system views, stored procedures, and functions
SQL dialect supported by SQL Server
Tabular Data Stream network protocol as described in Microsoft TDS documentation
Data types and conversions, where all data types are normalized automatically into equivalent SQL Server data types
Procedural programming constructs: IF, WHILE, DECLARE
Temporary tables (stored in-memory)
Selected SQL session environment variables
Security model supported by SQL Server
Authentication using Windows Integrated Authentication and SQL Server Standard Authentication (Mixed Mode)
Job scheduling model and accompanying stored procedures
Click SQL Guide for more details
Apache Spark engine is built-into Lyftrondata. No configuration is needed and a standalone instance of Spark is ready to use for querying and data storage. To utilize the power of Spark, users do not have to learn to code in Scala or SparkSQL, because everything is wrapped and hidden by Lyftrondata's emulation of Microsoft SQL Server.
Click Spark Standalone for more details.
Lyftrondata is a columnar data virtualization engine. Incoming rows of data are converted into columnar format for high parallelism and high performance processing. Outgoing results are converted into rows for compatibility with the SQL Server protocol.
Comparing to classic, flow-based ETL tools, Lyftrondata implements ETL responsibilities differently. Most processing requirements can be expressed using SQL and views, so Lyftrondata uses this approach.
Virtual databases are wrappers over a data source to provide direct access to data, or created over one or many supported data processing engines which may be used for data integration and caching.
Views may depend on each other and blend data using full power of SQL language.
Views created in Lyftrondata’s virtual databases are materializable (or cacheable).
Many processing-engine dependent strategies can be utilized, for example in-memory caching or persistent caching using table rotation.
SQL Server’s restrictions of “indexed views” do not apply to Lyftrondata.
The untrusted connection is defined in Lyftrondata as a target which the data cannot be pushed to “as-is”, but encrypted beforehand. Lyftrondata automatically encrypts the data for you while keeping the data types untouched.
Click Data Security for more details
The use of pre-computed aggregate data is a technique to address performance challenges in data warehouse (DW) systems. An aggregate retargeting mechanism redirects a query to an available aggregate table when possible.
Objectives of query retargeting:
- improve the performance of Data Warehouse and Data Marts
- improve architecture flexibility
- allow for a delay of optimization decisions until the performance requirements are fully known
- allow for ad-hoc optimizations for quick reaction to performance issues in production
Click Query Retargeting for more details
Flexible deployment model
Lyftrondata is fully web-based, no installation required. Lyftrondata comes with the following options:
- Self option in the Lyftrondata cloud, using either Azure or AWS region around the world
on-premise, using physical or virtual machine with Windows (server and desktop)
hybrid (mixed), on-premise and cloud
Lyftrondata offers a virtual database capability.
For each database, you can define the set of schemas you want to work with.
Tables can be imported from source databases using Metadata import.
This section describes the steps required to create a view for the virtual database. You can always define a view in the context of the previously selected virtual database.
Lyftrondata allows defining an index on a view if the underlying engine supports indexes. Managing indexes in Lyftrondata is available through GUI and SQL statements like CREATE / DROP INDEX.
Bulk operations allow you to perform the same action for many views and tables at the same time. When you select at least one view from the list, the new menu with the Bulk operations appears.
Click Bulk Operations for more details
Management tasks are object-type specific actions that can be executed against selected object. For example, a "Customers" view, depending on an underlying processing engine, may contain a task like "Rebuild persistent cache", but no "Evict from Spark in-memory cache".
Click Management Tasks for more details
Primary Key and Foreign Keys
Primary and foreign key capabilities of virtual tables and views within Lyftrondata.
When importing source metadata, all primary keys will be imported. Foreign keys will also be created for the tables if the referenced table is also being imported at the same time.
It is possible to create and edit primary and foreign keys after the metadata has been imported.
Primary and foreign keys can be used by tools that connect to Lyftrondata to optimize queries. For this reason, it is also possible to create primary and foreign keys for views.
Primary and foreign keys are constraints that are enforced within Lyftrondata by ensuring that the primary keys columns or columns are referenced by a foreign key column mapping cannot be deleted.
For more details click on Primary Key and Foreign Keys
Tagging allows you to tag databases, schemas, views and tables for easy access to the data. It helps in removing data silos.
For more details click on Tags.
This section guides you on how notification works in Lyftrondata.
For more details click on Notification.
Logging and Debugging
When debugging is desired, use logfile and verbosity connection string in your connection settings to handle the logging details.
For more details click on Logging and Debugging
This section shows you how Lyftrondata helps to parse Json and Xml paths automatically into relational format
Azure Cognitive AI