Impresa Interact: Driving Dynamic Connections and Intelligent Solutions
The name "Impresa" suggests enterprise, innovation, or a bold endeavor, while "Interact" emphasizes engagement and collaboration. Together, the name conveys a platform designed to facilitate seamless interaction, whether within an organization or between businesses and their stakeholders.
Key Features
Impresa Lakehouse
Impresa Lakehouse lives up to the promise to get insights from a variety of techniques like business intelligence and advanced analytics from all types of data that are growing explosively, and processing in real-time, with a more robust ability to deliver the right data at the right time. It can consolidate all the asset data across the Company and store as a single source of truth for the business where the entire lifecycle of these assets will be documented and tracked.
This covers all the asset managed by the utility company, including:
Water/Electric/Gas Transmission Operations
Water/Electric/Gas Distribution Operations
Water/Electric/Gas Network Operations
It delivers a comprehensive logical data and analytics platform with a complete suite of fully supported, solutions and technologies that can meet the needs of even the most sophisticated and demanding modern enterprise—on-premises, in the cloud, or within any hybrid scenario.
Data Ingestion
Impresa Lakehouse supports continuous, asynchronous, real-time or batched modes of data ingestion through variety of sources to handle large volume and variety of data including real time streaming feed.
Supports Structured, semi-structured and unstructured data :
Delimited Files -All supported file formats.
Support graph file formats.
File Extracts -All supported file formats (txt, csv, tsv, binary etc).
Tables populated in RDBMS DB Link - Tabular – Specified RDBMS.
Data Access through API -XML / JSON / BSON (e.g. Data access from No-SQL DB).
API providing file access path - Document / file links.
Direct Links - Abstract to end user – Applications will access the Data Warehouse directly.
Batch Processing. Periodically collect and group source data and send it to the destination system. Groups may be processed based on any logical ordering, the activation of certain conditions, or a simple schedule. Real-time processing (also called stream processing or streaming) involves no grouping at all. Data is sourced, manipulated, and loaded as soon as it’s created or recognized by the data ingestion layer. This kind of ingestion is more expensive since it requires systems to constantly monitor sources and accept new information. However, it may be appropriate for analytics that require continually refreshed data.
Data Storage
The processed data will be stored in the Data Warehouse. This Data is cleansed, transformed, and prepared with a definite structure and thus provides opportunities for end-user to use data as required by the Business. Following tools will be used for Data Storage :
OLAP Databases such as Microsoft SQL Server Analysis Services (SSAS).
PostgreSQL. Built-in streaming replication feature. Servers will be either a Master or a Standby server. Masters can send data, while Standby(s) are always receivers of replicated data. When cascading replication is used, Standby server(s) can also be senders, as well as receivers. Parameters are mainly for Sending and Standby servers, though some parameters have meaning only on the Master server.
Replication. SQL Server replication is a technology for copying and distributing data and database objects from one database to another and then synchronizing between databases to maintain consistency and integrity.
Optionally. Deploy R/Python code with SQL stored procedures making them accessible in ETL processes or to any application. Install and run any of the latest open source R/Python packages to build Deep Learning and AI applications on large amounts of data in SQL Server.
Data encryption mechanisms and security protocols such as SSL, HTTPS, and SSH.
Data Compression
Monthly snapshots
Data warehouse solution provides the Scalable System: we are providing the load balancing features, In the event of addition of new node, existing data should be rebalanced (auto balanced).
Meta Data & Governance
Meta Data & Governance Impresa provides options to track data lineage, audit and log events relevant to process these events. The Company can on a periodic basis analyze / view reports on this log / event analysis.
Users can understand the quality of the data using data profiling. Data quality refers to sanctity of data at the source, data correctness and validity post transformations, truth of data at the time of access.
Customer data will be secured. Without proper access no one can view the data, For Data movement will use encryption. Reporting layer we will incorporate Power BI security. we will implement DLP strategy.
Data Management: Able to manage data from the point of ingest to retirement – including retention, archiving, replication, compression, encryption/decryption and backup / purging. There should be a provision for entire data and
Meta Data & Governance
Meta Data & Governance Impresa provides options to track data lineage, audit and log events relevant to process these events. The Company can on a periodic basis analyze / view reports on this log / event analysis.
Users can understand the quality of the data using data profiling. Data quality refers to sanctity of data at the source, data correctness and validity post transformations, truth of data at the time of access.
Customer data will be secured. Without proper access no one can view the data, For Data movement will use encryption. Reporting layer we will incorporate Power BI security. we will implement DLP strategy.
Data Management: Able to manage data from the point of ingest to retirement – including retention, archiving, replication, compression, encryption/decryption and backup / purging. There should be a provision for entire data and
Meta Data & Governance
Meta Data & Governance Impresa provides options to track data lineage, audit and log events relevant to process these events. The Company can on a periodic basis analyze / view reports on this log / event analysis.
Users can understand the quality of the data using data profiling. Data quality refers to sanctity of data at the source, data correctness and validity post transformations, truth of data at the time of access.
Customer data will be secured. Without proper access no one can view the data, For Data movement will use encryption. Reporting layer we will incorporate Power BI security. we will implement DLP strategy.
Data Management: Able to manage data from the point of ingest to retirement – including retention, archiving, replication, compression, encryption/decryption and backup / purging. There should be a provision for entire data and
Benefits
Enhanced Data Accessibility
Access data at your fingertips, breaking down complex information into easy-to-understand insights that anyone in the organization can act upon.
Faster Decision Making
With real-time insights delivered directly to users, decision-makers can quickly assess information and respond to business needs immediately.
Increased Efficiency
Automates routine data queries and reduces the time spent searching for answers, enabling your team to focus on high-value tasks and strategic initiatives.
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