SnapDPL – the Answer to Enterprise Data & Analytics Challenges
Every analytic tool has its own data engines and provides different level of data management. ETL tools, however, don’t address all data problems such as complex data feeds or large scale data cleansing and rule application.
Data analysts spend 70% of their time to prepare data and 30% of their time to create reports and dashboards. For example, the survey files typically require about 40 minutes of manual data cleansing before they can be imported into the BI tool. If you need to do that once, you may not mind doing it. But if you need to do it repeatedly, an ETL tool like Snap DPL will allow you to automate the process and save a lot of time. SnapDPL provides drag and drop features that even a non-technical person can design the flow and automate the process.
How Does SnapDPL make the difference?
- Core features are data migration and data Integration.
- SnapDPL packs also support data blending, data cleansing, data filtering and formatting. There are a number of transformation functions which help users to create data pipelines.
- Non-technical person also can open and run pack.
- Packs can be scheduled to automate the process of execution.
- Jobs can be monitored using logs.
- Set of packs to be executed will fall under solution packs.
- Customer can open and run solution packs in the provided order from Execute Solution Pack Module.
- Customer can schedule all SQL related stored procedures, MSBI process etc.
- Migrating files from one folder to another automatically.
- Report Viewer Module allows to view the dashboard and to collaborate.
User data is stored in many different places and collected by many different tools — whether it’s in the apps, websites, sales exchanges, interactions with emails, or presence at events. SnapDPL is provided with ODBC connector to help you connect to different applications for data acquisition.
Data Cleansing involves detecting and correcting (or removing) corrupt or inaccurate data. In SnapDPL, cleansing includes following terminals
- Removing NULLs: Identifying NULL values and eliminating them
- Data Inconsistency: The redundant data, creates unreliable information because the chances of having a value changed in one file are high, but on the other file the value remains the same
- Data Scrubbing processes can be done through SnapDPL
Data sets are refined into simply what’s important for a user excluding data that can be repetitive, irrelevant or even sensitive. Different types of data filters can be used to amend reports, query results, or other kinds of information results.
Joining uncommon fields. This data linking follows a set of standards, which depends on the domain value of the data model used.
To display data in a more readable, localized format.
Flexible Data Scheduling
Once SnapDPL pack is done. It can be scheduled to keep data up to date which in turn updates the visualizations/ report.
Through this module, customer can view and share reports and visualizations.
SnapDPL Data Flow:
Here is an example dataflow which includes data blending, data cleansing, data joining, data formatting, transformations and mapping with target system and finally exporting to Crystal Reports
Data Movement Scenario
Use Case Scenarios
Scenario 1: Using direct DPL connector data is handed to DPL packages to move data through application layer in both directions
Scenario 2: Using Sybase replication server or SAP data services data from the SAP/Legacy application is replicated to a staging database
SnapDPL Manager moves the data needed for the BI with preformatted reports or visuals
SnapDPL Discovery has the ability to create new licensable application modules to capture data specific to customer scenario, leverage all connector capabilities, and enable a cloud portal for managing users and collaboration
With SnapDPL, you can:
- Connect to different data sources such as RDBMS, ERP, CRM and Social Media and many more
- Speed up the process of data preparation for analytic
- Automate the data flow and monitoring
- Export the prepared data set to the different analytic tool