Sidra Data Platform | Version 2021.R2: Glamorous Goldspur
This release comes with a lot of improvement features on several key modules of Sidra, especially on the data ingestion and the ML serving space. The data intake improvements have also been taken to the UI connectors (plugin) model, so the new versions of the connectors include multiple performance and functional improvements.
The plugin approach used for connectors (configuration of new data intake processes) has also been adapted to the Client Application space. This way, with this model, any new Client Application in Sidra that is developed following this plugin package approach can be easily created and deployed from a web UI.
On top of that, this release comes with important features around the operability of the platform, both from the web perspective, but also from the perspective of other operational tools, like the automation of API services monitoring and restarts.
With this release, we are also pleased to announce our new Sidra ideas portal, where you can have a place to share and vote ideas for Sidra.
- 1 What’s new in Sidra 2021.R2
- 1.1 Data intake functional and performance improvements
- 1.2 Client Applications UI assistant from Web and Client Applications detail page
- 1.3 Multiple DSUs installation processes
- 1.4 Plugin visibility management
- 1.5 Type Translations versioning and handling by plugins
- 1.6 Optimizations for handling tables in Databricks
- 1.7 Databricks version update
- 1.8 ML Model Serving updates
- 1.9 Oracle pipeline v1
- 1.10 Inclusion and exclusion modes in metadata extraction
- 1.11 DB2 connector plugin
- 1.12 API automation account for health monitoring
- 1.13 SQL Server metadata extraction for multiple databases
- 1.14 Sidra CLI improvements
- 1.15 Sidra operational Provider
- 1.16 Attributes popularity
- 1.17 Sidra ideas portal
- 1.18 API endpoint for EntityDeltaLoad
- 2 Issues fixed in Sidra 2021.R2
- 3 Coming soon…
What’s new in Sidra 2021.R2
Data intake functional and performance improvements
A big part of the engineering effort in this release has been dedicated to review and update important functional and performance enhancements on data intake pipelines. These new features have been introduced in order to support certain new scenarios, as well as to substantially improve the performance of the data intake processes.
Next is a list with all the updates included in this feature:
- Support automatic reload on Change Tracking expiration: The data ingestion incremental load process for SQL Server can use SQL Server native change tracking capabilities or use the Sidra-provided mechanism to track changes. In the case of using Change Tracking, Sidra now supports specific scenarios related to Change Tracking expiration. Before this change, when Change Tracking expired, tables in Sidra were marked as
True. However, this was not enough to force a reload until the field
EnableReloadwas set to 1 in the
EntityDeltaLoadtable. This feature now includes an improvement by which a new
AutomaticReloadproperty is used and populated in the AdditionalProperties Entity field. This way, when this field
AutomaticReloadis true, and
NeedReloadis true, a condition is triggered to reload the pipeline, no matter the value of
EnableReload.You can see more information about these fields in the EntityDeltaLoad and SQL Server incremental load mechanism documentation pages.
- Prevent access token from expiring in metadata extraction pipelines: Metadata extraction pipeline templates have been amended to prevent the Identity Server access token from expiring. This is especially important when there is a big amount of data to be managed in the pipelines for metadata extraction (for example, for the database metadata extraction pipelines).
- Handle truncate behaviour in source systems when not followed by new inserts: When a table is truncated in the source, Change Tracking expires, and the table will be marked for reloading. In this scenario, the table will be truncated in the data lake before inserting or merging the new data. Before this change, this worked fine if there was no new data, but if the table was truncated and no new data was inserted, the truncate on the data lake did not happen and the table kept the old data. Sidra now handles this scenario better, by ensuring that truncating a table from the source system is successfully reflected on all cases.
- Iteration performance improvements in Change Tracking mode: The incremental load with Change Tracking logic has been optimized, so the system does not have to iterate through all the tables, but only through the ones that have the actual changes. This has rendered a substantial performance improvement and cut down pipeline execution times substantially.
- Make dummy assets optional: Dummy assets or empty assets are an internal construct used in Sidra when there are no changes in the source data during incremental loads. Dummy assets are needed to enable synchronization with the downstream Client Applications, which are configured to launch data synchronization pipelines with Sidra Core when all Assets have been created. Via these empty Assets it is signaled when an actual finished data intake process of the Entity happens. This enhancement makes the creation of these dummy Assets optional (and set to disabled by default), to adapt for scenarios where the periodicity of data intake into the data lake may be much more frequent than the periodicity required at the Client Application end. This means that, for those cases where dummy Assets approach was being followed, it will need to be explicitly configured as active in future deployments.
Client Applications UI assistant from Web and Client Applications detail page
In the previous release of Sidra, 2021.R1, data connectors UI feature was launched to simplify the configuration of new data intake processes via Sidra Web. Although using the term connectors to refer to the different programs to connect to the data source systems (e.g. SQL Server, Azure SQL database), internally such programs are packaged in a plugin model. For creating and configuring data intake processes, we use plugins of type
connector. Such plugins are code assemblies that get downloaded, installed, and executed from the Web UI.
A very important advantage of plugins is that their release lifecycle can be decoupled from Sidra main releases life-cycle, which increases delivery speed for new plugins and reduces dependencies. You can see more details about the plugin approach in Sidra connectors documentation. With 2021.R2 the whole plugin management and architecture has been adapted to allow the deployment of Client Applications from the Web. In this case, the plugins used are of type
This is a major achievement of this release. Along with subsequent versions of Sidra, we plan to announce and release new plugins of type
Client Application. We plan to migrate existing Client Application templates with this new plugin model, so that the deployment (from a given template) of a Sidra Client Application can be performed just by filling out a set of UI wizard steps. The same approach is ready to be followed as well for implementing future new customer Sidra Client Applications.
In addition to this, Sidra Web now incorporates a Client Application detail page that exposes the metadata information about Client Applications. Clicking on each Client Application, a Client Application detail page is opened, which displays metadata fields like the Data Catalog for Providers and Entities: short description, business owner and a detailed description field allowing markdown edition.
This view will be the entry point towards adding, in subsequent releases, more operational details and actions on Client Applications.
Multiple DSUs installation processes
Sidra architecture relies on the concept of Data Storage Unit (DSU) as the atomic infrastructure unit for data processing (data intake, data storage, data transformation, data indexing and data ingestion) in Sidra. Each Data Storage Unit allows separation of concerns and even regional data segregation if needed.
After releasing Sidra CLI tool for installation and deployment of Sidra, there was a pending requirement to migrate the new deployment process to separately being able to install a second or subsequent DSUs. This feature incorporates a specific deployment script and process for installing additional DSUs on top of the default DSU which comes with any Sidra installation. This feature is subject to the availability of dependant Data Storage Unit Azure services in each region, and this includes Databricks service regional availability.
Plugin visibility management
This feature allows decoupling plugin development and release cycles, both for connectors and for Client Applications, by managing the visibility of any plugin and plugin version at each customer and Sidra installation level. Through this feature, it is now possible for customers to limit the visibility of plugins and plugin versions to certain Sidra installations. This enables scenarios of early testing and validation of new plugins being developed.
Type Translations versioning and handling by plugins
TypeTranslation table stores type mapping and transformation rules for the different stages of the Attributes processing when data sources are configured and ingested in Sidra. You can see more information about these tables in Data ingestion documentation and About Sidra connectors pages.
The use of such table has been extended to host all types of type mapping and transformation rules between database source systems, ADF and Databricks. Each plugin now handles the configuration and versioning of a set of type translation rules. These type translation rules are then applied during the data extraction and ingestion processes.
Each plugin persists the needed data types translations and rules when being installed. The responsibility to populate this
TypeTranslation table also lies now on each specific plugin, instead of being centralized in Sidra Core.
This means that the different type mappings for each connector plugin are now completely decoupled from Sidra Core release process and tied solely to the corresponding plugin release process.
Optimizations for handling tables in Databricks
As part of the operational hardening of the platform, we have incorporated a set of enhancements related to creating and handling Databricks tables in certain scenarios. These are the set of changes included as part of this optimization:
- We have incorporated a safeguard to prevent names longer than 128 characters in temporary tables when performing metadata extraction.
- We also have performed renaming of Databricks tables to avoid name conflicts when there is an overlap of table names across different sources configured logically to belong to the same Provider.
- The new naming convention uses this format:
- Additionally, we have incorporated a deduplication rule for the names of the Attributes in Databricks. We detected that some client sources may have multiple field names that are even single, non-supported characters in Databricks, e.g.,
*, etc. These names are transformed into the
_character when moving to Databricks. This means that many of these Attributes may end up having the same name. In order to avoid this, the change consists of appending a number suffix to the names of the columns in Databricks, e.g.,
_2, etc., so there is no duplication of column names.
Databricks version update
Databricks runtime version in Sidra has been updated to 8.3. This comes with interesting updates and features, like Python 3.8, faster cluster startup times, many performance improvements and as JSON operators in SQL, among others.
ML Model Serving updates
We have performed an extensive review on Sidra’s ML Model serving framework and capabilities, to adapt to the fast changing landscape and technical evolution of the key services underneath this module. These are the main updates performed as part of this review: – Updated Python Version to 8.3.3. – Updated MLFlow to 1.18. – Updated AzureML components to 1.32. – Updated the ML Deployment notebooks to accommodate to the breaking changes in AzureML and MLFlow updates. – Updated the ML tutorial and code examples.
Oracle pipeline v1
We have implemented our first version of an Oracle pipeline template following Sidra design guidelines. This data intake process has not been implemented yet as a plugin of type connector, but this is planned for the next release of Sidra. The main characteristics of this Oracle pipeline are the following ones:
- The source system version to be supported is 19.X (latest).
- A metadata extraction pipeline has been implemented in order to get the schema for source system tables and automatically create the needed Sidra metadata structures (Entities and Attributes).
- This pipeline supports both initial load and incremental load mechanisms. For incremental load, the pipeline relies on Sidra-backed EntityDeltaLoad table. Native change tracking mechanisms from Oracle are expected to be implemented in further versions of this pipeline/plugin.
- Type translations between source systems and Sidra data lake are versioned and handled by plugin-created tables. Plugin installation now adds the needed type translations for metadata and data ingestion process. See the new feature for Type Translations above.
Inclusion and exclusion modes in metadata extraction
Data intake pipelines for database source systems (e.g. SQL, DB2) included a tables exclusion parameter in order to configure a list of objects to exclude from the metadata extraction pipeline. With this Sidra release, we have incorporated an enhanced object selection mechanism, through which users can choose a list of objects to include, or a list of objects to exclude. By default, inclusion mode with all objects is configured. This change has been taken to existing database pipelines and plugins: SQL Server, Azure SQL, DB2, plus the Oracle pipelines. Internally, objects to include or exclude are persisted on the
LoadRestrictionObject tables in Sidra Core database. You can see documentation on LoadRestrictions and About connectors feature documentation.
DB2 connector plugin
Sidra now incorporates support for DB2 in the connectors framework model. This allows to set up a data intake process from DB2 database source systems just by following a few-steps configuration wizard. This version of the plugin comes with Sidra-backed incremental load mechanism.
API automation account for health monitoring
Sidra incorporates an out of the box capability to run different operational runbooks to automate the periodic health monitoring of API services and attempt automatic restart in case they are down. This is achieved via Azure automation account implementation.
SQL Server metadata extraction for multiple databases
This release comes with a new version of the SQL Server metadata extraction pipeline, which has also been released as a new version of the SQL Server connector plugin. This version allows to configure the metadata extraction from multiple databases inside an SQL Server at once, so they logically link in Sidra to the same Provider.
This change has been done in the metadata extraction pipeline of the SQL Server plugin. Now, in the SQL Server plugin, if a database is not specified, the default database is be taken as
master, which now forces to loop through the whole list of databases inside the same SQL Server and extract metadata from all of them.
This greatly simplifies and streamlines the process of data intake, as now we can configure a data source where there is a big number of different databases sitting in an SQL Server with just a few steps.
Sidra CLI improvements
Sidra CLI tool for installations and updates has been reviewed to ensure all commands are idempotent by default. This means that the different commands can be executed several times without needing to manually remove any data from Azure. This improvement minimizes the operational issues that can happen when there is any unexpected problem when running the commands. More specifically, the
aadaplications command has been made idempotent, ensuring that all AAD applications are removed automatically before re-running the command, with no manual intervention required.
Sidra operational Provider
Sidra now incorporates an internal Provider containing relevant data sources originating internally in Sidra from tracing and operational modules.
The first version of this internal Sidra Provider includes the Log database in Sidra Core. Log database stores a copy of the logs (traces) of the platform, by applying a set of filters. There are several tables where the logs are located. This Provider improves the performance of Log database, by moving the older traces to the data lake (from which it can be queried and explored through Databricks). This allows optimizing the Log database by reducing the retention period in the database to the last 30 days. The full Log history is available in Databricks tables.
Sidra Data Catalog now includes the ability to see the popularity for all Attributes in the Attribute list and details page. Before this feature, just the top three Attributes popularity was displayed as a sample, in the Entity details page.
Sidra ideas portal
At Sidra, we are hugely interested in hearing your voice! We are pleased to launch with this release a new Sidra ideas portal. From this, you can suggest and vote ideas on Sidra roadmap. The ideas portal is also a great place to see what is coming and get a glimpse on mostly shared and voted ideas. We welcome your suggestions and feedback!
API endpoint for EntityDeltaLoad
Sidra API now incorporates API endpoints to serve create, read, update and delete operations on
EntityDeltaLoad table. This table is a key piece in Sidra metadata DataIngestion schema tables. This table fully tracks the needed data for managing incremental loads from source systems, which is independent of any native supported change tracking mechanisms.
See Sidra documentation for DataIngestion tables for more details about the structure and usage of this table.
Issues fixed in Sidra 2021.R2
Access to the complete list of resolved issues and relevant changes in Sidra 2021. R2, here.
This 2021.R2 has been a very important release for Sidra, as it has added new connector plugins using the previously released plugin model. But not only that: this release represents an important step towards making the existing pipelines and data ingestion robust enough to handle many new scenarios.
The architectural foundations for Client Applications plugin model, therefore, the ability to deploy Client Applications from the web, has also been achieved as part of this Sidra version. This lays down the path towards a streamlined way of packaging Client Applications so that its deployment is very simple to execute.
As part of the next release, we are planning to deliver our first Sidra-owned Client Application plugin, with a basic SQL Client Application as the first plugin of its type. Also, we plan to proceed further with the plugin model to allow further scenarios of plugin update configuration or plugin upgrades.
New plugins will be added to the gallery of connectors following a release cycle that no longer needs to be tied to Sidra Core release cycle.