Data is a real wealth of a business. With effective and efficient usage of Data, business processes can be improved ,it will help in finding potential customers and keep track of existing customers, monitor competitors, target advertising, and innovate. But just data collection isn’t enough. For it to be powerful, data must be analyzed and then strategically applied. To accomplish this, a complete and effective Business Analytics solution is required.

A complete analytics or business intelligence solution may have various stages depending on customer’s requirements for e.g. data replication,data integration, ETL etc. Oracle has a complete set of products available for on-premises and cloud implementations to fulfil all of these requirements. This blog is about how to implement an end-to-end Cloud/on-premises Analytics solution using Oracle product domain.

Let’s have a glance on what should be the requirements of an end-to-end business intelligence and analytics solution.

  • List of Data sources which provides the data to analyze, these can be Databases, cloud applications or on-premises applications, File server etc.
  • Requirement of real time data replication if customer need real time analytics.
  • Data Integration to blend data of various data sources which is required for reporting/analytics.
  • ETL process to transform the data into required format and populate data warehouse.
  • Data quality process to improve, protect and govern data quality
  • Dimension data modelling to design Star or Snowflake model with Dimensions and fact tables in Data warehouse.
  • Aggregation layer in data warehouse based on reporting requirements for e.g. Monthly, Quarterly or yearly reports.
  • Machine learning models to predict the future and to perform predictive analytics.
  • Analytics, Reports & Dashboards.
Oracle Business Intelligence and Analytics Solution
End-to-End Analytics solution

Oracle has specialised tools to fulfil each of these requirements and implement all of these processes, these products are available for on-premises as well as cloud implementations on OCI.In the following section you will find a short description of these tools and software components such as:

Oracle GoldenGate

Process – Real time data replication.

Oracle GoldenGate is a software for on-premises customer and managed service for cloud customer to provide a real-time data mesh platform, which uses replication to keep data highly available, and enabling real-time analysis. Customers can design, execute, and monitor their data replication and stream data processing solutions.

Oracle GoldenGate captures and delivers real-time changed data to on-premises or cloud-based data warehouses, lakes and object stores, reporting systems, and other online transaction processing (OLTP) databases with minimal source system impact. It provides real-time capture and delivery of changed data between OLTP and data warehouse systems. Oracle GoldenGate 21c integrates easily with Oracle Data Integrator Enterprise Edition and other extract, transform, and load (ETL) solutions. It is certified to capture from and deliver to Oracle Exadata, Oracle Autonomous Database (ADW, ATP, JSON), and Oracle Cloud@Customer platforms to enable real-time data warehousing or data consolidation solutions.

To get more details please refer to this Data Sheet & documentation.
On-premises
Oracle GoldenGate Data Sheet
Oracle GoldenGate Documentations

Cloud
Oracle GoldenGate Data Sheet
Oracle GoldenGate Documentations

Oracle Data Integrator

Process – Data Integration & ETL.

Oracle Data Integrator (ODI) loads and transforms data faster into data warehouses by leveraging the power of the target database instead of relying on a conventional ETL server. Pre-built connectors simplify integration by automating manual integration tasks needed to connect databases and big data. Oracle Data Integrator Enterprise Edition is critical to leveraging data integration initiatives on-premises or in the cloud, such as Big Data management, Service Oriented Architecture and Business Intelligence.An easy-to-use user interface combined with a rich extensibility framework helps Oracle Data Integrator Enterprise Edition improve productivity, reduce development costs and lower total cost of ownership among data-centric architectures. Oracle Data Integrator Enterprise Edition is fully integrated with Oracle Fusion Middleware, Oracle GoldenGate, Oracle Database, Oracle Big Data Appliance and Exadata to put data at the center of your enterprise.

To get more details please refer to this ODI Data Sheet & documentation.
On-premises
Oracle Data Integrator Data Sheet
Oracle Data Integrator Documentations

Cloud
Oracle Data Integrator on OCI Marketplace Data Sheet

Oracle Enterprise Data Quality

Process- Data quality & governance.

The Oracle Enterprise Data Quality family of products helps organizations achieve maximum value from their business-critical applications by delivering fit-for-purpose data.These products also enable individuals and collaborative teams to quickly and easily identify and resolve any problems in underlying data. The product is available for on-premises customer as well as for Cloud customer on OCI Marketplace.

Quick to deploy and easy to use, Oracle Enterprise Data Quality products bring the ability to enhance the quality of data to all stakeholders in any data management initiative. Oracle Enterprise Data Quality products cover:
• Profiling, Audit and Dashboards
• Parsing and Standardization
• Match and Merge
• Case Management
• Address Verification
• Product Data Capabilities

Oracle Enterprise Data Quality can be used with any data integration or ETL system, but is pre-integrated with Oracle’s flagship product for data movement and transformation—Oracle Data Integrator. This integration enables customers to take advantage of Oracle Enterprise Data Quality products in their integration projects for simple and rapid deployment as part of a complete data integration solution.

To get more details please refer to this Data Sheet & documentation.

Oracle Enterprise Data Quality Data Sheet
Oracle Enterprise Data Quality Documentations

Oracle SQL Developer Data Modeler

Process – Data Modeling.

Oracle SQL Developer Data Modeler is a graphical tool that enhances productivity and simplifies data modeling tasks. Using Oracle SQL Developer Data Modeler users can create, browse and edit, logical, relational, physical, multi-dimensional, and data type models. The Data Modeler provides forward and reverse engineering capabilities and supports collaborative development through integrated source code control. Using Oracle SQL Developer Data Modeler users can connect to all available Oracle Database releases . There is also support for IBM DB2 LUW V7 and V8, IBM DB2/390, Microsoft SQL Server 2000 and 2005 or a standard ODBC/JDBC driver for selective import of database objects. It supports developers on multiple platforms such as Windows, Linux, macOS X.

KEY FEATURES

  • Create, browse, and edit database models.
  • Synchronized forward and reverse engineering between Logical and Relational models.
  • Model compare and merge facilities.
  • Diagram support for subject areas, and a choice of notations.
  • Large model printing facilities.
  • Name standardization and design rules.
  • Collaborative development through integrated version control.

To get more details please refer to this Data Sheet & documentation.

Oracle SQL Developer Data Modeler Data Sheet
Oracle SQL Dwveloper Data Modeler Documentations

Oracle Data Warehouse

Process – data warehousing.

Oracle Database 19c Enterprise Edition

Oracle Database 19c builds upon the innovations of previous releases such as Multitenant, In-Memory, JSON support, Sharding and many other features that enable Oracle’s Autonomous Database. This latest release also introduces new functionality, providing customers with a multi-model enterprise-class database for all their typical use cases, including:
• Operational database use cases such as traditional transactions, real-time analytics, JSON document stores and Internet of Things (IoT) applications.
• Analytical database use cases such as traditional and real-time data warehouses and data marts,big data lakes and graph analytics.

Oracle database 19c is an optimized platform for all data warehousing applications, it has various features designed for data warehouse workload. Oracle database 19c is available for both on-premises and cloud implementations.

Oracle Autonomous Data Warehouse

Oracle Autonomous Data Warehouse is Oracle’s fully managed database tuned and optimized for data warehouse workloads with the market-leading performance of Oracle Database. This is only available for cloud implementations. It delivers a completely new, comprehensive cloud experience for data warehousing that is easy, fast, and elastic.
EASY

  • Fully autonomous database
  • Automated provisioning, patching and upgrades
  • Automated backups
  • Automated performance tuning

FAST

  • Built on Exadata: high performance, scalability and reliability
  • Built on key Oracle Database capabilities: parallelism, columnar
  • processing, compression

ELASTIC

  • Elastic scaling of compute and storage, without downtime
  • Pay only for resources consumed

To get more details please refer to this datasheet and documentation.

Oracle Database 19c

Oracle Database 19c Data Sheet
Oracle Database 19c Documentations

Oracle ADW
Oracle Autonomous Data Warehouse Data Sheet
Oracle Autonomous Data Warehouse Documentations

Oracle Analytic Views

Process- Aggregation

Analytic views are objects of Oracle Database. Analytic views provide a fast and efficient way to create analytic queries of data stored in existing database tables and views. Analytic views organize data using a dimensional model. They allow you to easily add aggregations and calculations to data sets and to present data in views that can be queried with relatively simple SQL.

Like standard relational views, analytic views:

  • Are metadata objects (that is, they do not store data)
  • Can be queried using SQL
  • Can access data from other database objects such as tables, views, and external tables
  • Can join multiple tables into a single view

Analytic views also:

  • Organize data using a rich business model that has dimensional and hierarchical concepts
  • Include system-generated columns with hierarchical data
  • Automatically aggregate data
  • Include embedded measure calculations that are easily defined using syntax based on the business model
  • Include presentation metadata

The definition of an analytic view includes navigation, join, aggregation, and calculation rules, thus eliminating the need to include these rules in queries. Rather than having simple tables and complex SELECT statements that express joins, aggregations, and measure calculations, you can use simple SQL to query smart analytic views. This approach has several benefits, including:

  • Simplified and faster application development; it is much easier to define calculations within analytic views than it is to write or generate complex SELECT statements
  • Calculation rules can be defined once in the database and then be re-used by any number of applications; this provides end-users with greater freedom of choice in their use of reporting tools without concern for inconsistent results

To get more details please refer to this documentation.
Oracle Analytic Views Documentations

Oracle Analytics Server/ Oracle Analytics Cloud

Process – Analytics & Reporting.

The Oracle Analytics platform provides the capabilities required to address the entire analytics process from data ingestion and modeling, through data preparation and enrichment, to visualization and collaboration without compromising security and governance. Embedded machine learning and natural language processing technologies help increase productivity and build an analytics-driven culture in organizations. Start on-premises with Oracle Analytics Server or in the cloud with Oracle Analytics Cloud, Oracle Analytics supports a hybrid deployment strategy, providing flexible paths to the cloud. In terms of functionality and features Oracle Analytics Cloud(OAC) and Oracle Analytics Server(OAS) are similar, OAC is suitable for Cloud implementations whereas OAS is for on-premises customers.
Oracle Analytics is an integrated platform that addresses all analytic needs across the entire workflow start from connecting to various Data Sources, Data Modeling, Data Preparation, Explore & Visualization to Collaboration with team and business users.

To get more details please refer to this datasheet and documentation.

Oracle Analytics Server
Oracle Analytics Server Data Sheet
Oracle Analytics Server Documentations


Oracle Analytics cloud.
Oracle Analytics Cloud Data Sheet
Oracle Analytics Cloud Documentations

Oracle Machine Learning

Process – Machine learning & future predictions.

Oracle delivers the data management platform that enables data science teams to analyze data where it resides at scale and with minimal data movement, whether in the database or data lake. Oracle Machine Learning is a key part of Oracle’s converged database strategy, which combines the functionality of multiple special-purpose databases while operating faster and with greater reliability. With a converged database and autonomous database self-service tools, users reduce complexity and management for an end-to-end optimized experience.

Machine Learning in Oracle Database supports data exploration, preparation, and machine learning modeling at scale using SQL, R, Python, REST, AutoML, and no-code interfaces. It includes more than 30 high-performance in-database algorithms producing models for immediate use in applications. By keeping data in the database, organizations can simplify their overall architecture and maintain data synchronization and security. It enables data scientists and other data professionals to build models quickly by simplifying and automating key elements of the machine learning lifecycle.

Similar to Oracle database machine learning is available on Autonomous Data Warehouse as well for cloud implementations.

To get more details please refer to this data sheet documentation.

On-premises

Oracle Machine Learning Data Sheet
Oracle Machine Learning Documentations

Cloud

Oracle Machine Learning Documentations