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Sesame Software’s Data Management Platform provides the foundation for a cost-effective, highly scalable Data Lakehouse SANTA CLARA, Calif. – February 24, 2022 / PR Newswire / – Sesame Software , the innovative leader in Enterprise Data Management and creator of Sesame Software, today announced its Lakehouse Platform, providing simplified data architecture by eliminating the data silos that traditionally separate analytics and data science. The Lakehouse Platform combines the best elements of data lakes and data warehouses — delivering data management and performance typically found in data warehouses with the low-cost, flexible object stores offered by data lakes. “A streamlined method to access data is difficult to achieve with a separate cloud data warehouse and data lake.” – says Rick Banister, founder, and CEO of Sesame Software. “A data lakehouse offers the best of both worlds – by replacing data silos with a single home for structured, semi-structured, and unstructured data, Sesame Software provides a solid and scalable Lakehouse foundation.” Sesame Software Key Benefits: Provides performance and governance required to support all types of data workloads Hyper-threaded technology delivers massive scale and speed  Runs operations on one simplified architecture, avoiding complex, redundant systems Supports advanced analytics, and lower total cost of ownership Sesame Software: The Lakehouse Foundation  Sesame Software delivers reliability, security, and scalability on your data lake, making it the top data ingest platform to handle vast amounts of data from a wide variety of SaaS applications and databases . With plug-in architecture, new data sources can be added with configuration-only deployment. The platform’s scalable architecture continuously evolves with organization-spanning data needs, allowing users easy access to their data and the ability to use how they see fit.  Request a demo to learn more about Sesame Software’s Data Lakehouse Platform.  About Sesame Software  Sesame Software is the Enterprise Data Management leader, delivering data rapidly for enhanced reporting and analytics. Sesame Software’s patented data platform offers superior solutions for data warehousing, integration, backup, and compliance to fit your business needs. Quickly connect to SaaS, on-premise, and cloud applications for accelerated insights. Learn more today ! Media Contact: marketing@sesamesoftware.com

The Data Lakehouse has emerged ​​as a new data management architecture across many organizations and use cases. This post describes this new architecture and its advantages over previous methods. In this article we will cover: Traditional Data Warehouses and Data Lakes What is a Lakehouse? Lakehouse Used for Business Intelligence Sesame Software Data Warehouse Platform Traditional data warehouses have a long history in decision support and business intelligence applications. While data warehouses are great for structured data, many modern enterprises have to deal with unstructured, semi-structured, and data with a wide variety, velocity, and volume. Data warehouses are not suited for many of these use cases, and they are certainly not the most cost-efficient. As companies collected large amounts of raw data from many different sources, there was an increasing need for a single system to house data for many other analytic products and workloads. In response to this need, companies began building data lakes. While suitable for storing data, data lakes lack some critical features: they do not support transactions or enforce data quality, resulting in a lack of data consistency.   The need for a flexible, high-performance system hasn’t diminished. Companies require systems for diverse data applications, including SQL analytics, real-time monitoring, and data science.  Most of the recent advances have been in better models to process unstructured data, but these are the types of data that a data warehouse is not optimized for. A common approach is to use multiple systems – a data lake, data warehouses, and other specialized systems. However, this introduces complexity and delays, as data professionals need to efficiently move or copy data between different systems. Today, organizations that work with various data sets have yet another option for storage architecture: a hybrid architecture called the “data lakehouse” approach. What is a Lakehouse? Like a data lake, a data lakehouse is built to unify data – both structured and unstructured. Businesses that can now benefit from working with unstructured data only need one data repository rather than requiring both warehouse and lake infrastructure. Lakehouses are enabled by a new system design: implementing similar data structures and data management features to those in a data warehouse directly on top of low-cost storage in open formats.  When organizations use both, generally, data in the warehouse feeds BI analytics. In contrast, data in the lake is used for data science – including artificial intelligence (AI) such as machine learning and storage for future, undefined use cases. Data lakehouses enable structure and schema like those used in a data warehouse to be applied to the unstructured data of the type typically stored in a data lake. This means that data users can access the information more quickly and start putting that information to work. Those data users might be data scientists or, increasingly, workers in any number of other roles that are increasingly seeing the benefits of augmenting themselves with advanced analytics capabilities. These data lakehouses might use intelligent metadata layers – that act as a middle ground between the unstructured data and the data user to categorize and classify the data. By identifying and extracting features from the data, it can effectively be structured, allowing it to be cataloged and indexed just as if it was nice, tidy structured data. Lakehouse for Business Intelligence Organizations are increasingly looking to unstructured data to inform their data-driven operations and decision-making simply because of the richness of the insights extracted from it. So who is the data lakehouse architecture built for? One key group is organizations looking to graduate from BI to AI. Organizations are increasingly looking to unstructured data to inform their data-driven operations and decision-making simply because of the richness of the insights extracted from it.  Yes, you could put all of that data into a data lake. However, there would be significant issues of data governance to address – such as the fact you’re likely dealing with personal information.  A lakehouse architecture would address this by automating compliance procedures – perhaps even anonymizing data where needed.  Unlike data warehouses, data lakehouses are inexpensive to scale because integrating new data sources is automated – they don’t have to be made to manually fit with the organization’s data formats and schema. Data can be queried from anywhere using any tool, rather than being accessed through applications that can only handle structured data (such as SQL).  The data lakehouse approach is one that’s likely to become increasingly popular as more organizations begin to understand the value of using unstructured data together with AI and machine learning. In the data analytics journey, it’s a step up in maturity from the combined data lake and data warehouse model.  Over time lakehouses will close these gaps while retaining the core properties of being simpler, more cost-efficient, and more capable of serving diverse data applications.  Sesame Software Lakehouse Platform Sesame Software’s Lakehouse Platform combines the best elements of data warehouses and data lakes. Sesame Software delivers data management and performance typically found in data warehouses with the low-cost, flexible object stores offered by data lakes. This unified platform simplifies your data architecture by eliminating the data silos that traditionally separate analytics and data science. Request a demo to learn more on how Sesame Software can provide you with a secure and scalable foundation for your Lakehouse.

Sesame Software’s Data Management Suite, empowers Vertica Users to Integrate Data Across All Platforms SANTA CLARA, Calif. – February 15, 2022 / PRNewswire / — Sesame Software , the innovative leader in Enterprise Data Management and creator of Sesame Software, today announced its rapid data connector for Vertica. Sesame Software provides ETL, ELT, transform, and complete integration of Vertica data – on-prem, on-cloud, or anywhere in between. Sesame Software enables users to integrate Vertica data with a wide variety of data sources to ensure company data is broadly available. With Sesame Software, users can construct data pipelines in minutes and replicate data to virtually any destination, including cloud data warehouses such Amazon Redshift, Google BigQuery, Snowflake, Microsoft Azure, as well as on-premises databases such as Oracle, Microsoft SQL Server, and MySQL to name a few. Key Benefits Include: Connect internal and external data sources to Vertica in minutes Synchronize Vertica with Salesforce, Oracle, and other back-office systems Access the complete Vertica data model for a comprehensive view of data Unify IT systems and auto-scale business processes across applications Integrate data into analytical and operational layers without heavy, repetitive lifting  No coding, data mapping, or maintenance required Sesame Software: The Complete Solution Sesame Software combines integration , replication , data warehousing , and compliance to cover all of your data needs. This unified platform simplifies your data architecture by eliminating the data silos that traditionally separate analytics and data science. Sesame Software helps data teams simplify data movement with improved data reliability and cloud-scale production operations to help build the best foundation for data analysis.  Request a demo to learn more about how Sesame Software integrates Vertica with the data sources and applications within your tech stack! About Vertica  Vertica supports a variety of computational processing, BI, and analytics applications. Designed to build intelligent applications using a scalable, hybrid database platform that has everything built in—from in-memory performance and advanced security to in-database analytics. About Sesame Software  Sesame Software is the Enterprise Data Management leader, delivering data rapidly for enhanced reporting and analytics. Sesame Software’s data platform offers superior solutions for data warehousing, integration, as well as backup, and compliance to fit your business needs. Quickly connect to SaaS, on-premise, and cloud applications for accelerated insights. Media Contact: Sesame Software marketing@sesamesoftware.com

Sesame Software’s Data Management Platform, provides scalable integration for Siebel users with end-to-end connectivity to cloud and on-premise applications SANTA CLARA, Calif. – February 03, 2022 / PRNewswire / — Sesame Software , the innovative leader in Enterprise Data Management and creator of Sesame Software, today announced its high-volume data connector for Siebel — helping organizations take full advantage of Siebel’s complex capabilities by connecting Siebel with other enterprise applications. Sesame Software’s Data Connector for Siebel provides users with a rapid way to connect Siebel implementations to a wide variety of endpoints. With patented hyper-threaded technology, Siebel users are ensured their data will move at the fastest possible speed. Managing various technology platforms makes it difficult to view data cohesively and make critical business decisions. As a solution to this problem, Sesame Software integrates Siebel with multiple platforms to achieve an overall view of the valuable data contained within.  Key Benefits Include: Connect a wide variety of cloud and on-premise applications to Siebel in minutes Synchronize Siebel with Salesforce, Oracle, and other back-office systems Access the complete Siebel data model for a comprehensive view of data Create flexible hybrid CRM deployments and faster CRM migrations Unify IT systems and automate business processes across applications Integrate data into analytical and operational layers without heavy, repetitive lifting  No coding, data mapping, or maintenance required Sesame Software: The All-in-One Solution Sesame Software is a multifaceted solution that combines integration , replication , data warehousing , and compliance to cover all of your data needs. The platform’s scalable architecture continuously evolves with organization-spanning data needs, allowing users easy access to their data and the ability to use how they see fit.  Request a demo to learn more about how Sesame Software allows you to seamlessly flow data across Siebel and any data source or application in your tech stack! About Sesame Software  Headquartered in Santa Clara, California, Sesame Software is the Enterprise Data Management leader, delivering data rapidly for enhanced reporting and analytics. Sesame Software’s patented Sesame Software suite of products offers superior solutions for data warehousing, integration, as well as backup, and compliance to fit your business needs. Quickly connect to SaaS, on-premise, and cloud applications for accelerated insights. Learn more today ! Media Contact: Sesame Software marketing@sesamesoftware.com

Replicated data is copied from one record system to another, which acts as a backup system. Working copies of source databases are beneficial for a number of reasons. Data replication helps organizations increase availability, accessibility, backup, and disaster recovery. We will cover the following topics in this white paper: The most common reasons to use data replication How to use data replication Benefits of data replication Methods to accomplish your goals How to Use Data Replication Enhance the Availability of Data The distribution of data across networks enhances fault tolerance and accessibility, especially across global organizations. The replication of data across multiple nodes in a global network increases the resilience and reliability of systems. Access Data for Reporting and Analytics Businesses with data-driven strategies collect and store data from multiple sources in data warehouses . Reports that span multiple applications. Business intelligence users can now see their corporate data in a 360-degree view.  Increase Data Access Speed It is possible for users to experience some latency when accessing data from one country to another in organizations with multiple branch offices. By placing replicas on local servers, users are able to access data more quickly and execute queries more quickly. Enhance the Performance of the Server Additionally, replicating data can optimize server performance. Utilizing the original source system’s database can strain the system’s resources when it comes to data analytics and business intelligence. This can cause performance issues with original transactional systems. The administrator can save processing cycles on the primary server for more resource-intensive writes by routing all read operations to a replica. Replication of databases reduces the load on the source application server. The performance of the network is improved by dispersing the data among the nodes of the distributed system.   Ensure Disaster Recovery A data breach or hardware malfunction can lead to businesses losing data. During a disaster, valuable data of employees or clients may be compromised. By maintaining accurate backup copies at well-monitored locations, data replication facilitates the recovery of lost or corrupted data. In addition, a recovery tool is essential for this purpose, one that can retain backups for varying lengths of time according to data retention best practices and patchwork of laws governing data retention. How Data Replication Works The replication process involves copying data from different sources to multiple destinations. It is possible, for example, to copy data between two on-premises hosts, between hosts in different locations, to multiple storage devices on the same host, or to or from a cloud-based host. A master source can replicate data according to a schedule or in real-time, or it can be changed or deleted. There is a challenge in finding a solution that works with all of your data without needing different solutions for different applications. Stay away from niche products that cater to just one or two applications. Data Replication Benefits The sharing of data across multiple hosts or data centers is facilitated with data replication, which distributes network load over multiple multiple-site systems by making data available on multiple hosts. Among the benefits organizations can expect are: Availability and reliability: If one system fails due to faulty hardware, a malware attack, or another issue, the data can be accessed from another site. Having the same data in multiple locations can reduce data access latency since necessary data can be retrieved closer to where the transaction is taking place. Support for business intelligence with replication of data: Replicating data to a data warehouse enables distributed analytics teams to work on a common project. Test system performance is improved: Data replication facilitates the distribution and synchronization of data for test systems that require quick access to data. Data Replication Methods Let’s examine the methods of data replication in relation to latency first. Real-time replication is necessary in some use cases, such as having a standby database ready in case a database server fails. Standby Databases In the event of a server failure, standby databases provide redundancy. This can be caused by a corrupted filesystem or a broken network path. A hot backup database can automatically be switched to be the active database when needed, providing an extra layer of protection to keep systems running without any downtime. Every new transaction in the database can be replicated to a Standby Database in several database platforms. The process is known as Change Data Capture (CDC) if it is done in real-time. Instead of polling the data directly, CDC uses database logs on the source database. Replication in Near-Real-Time Near-real-time replication is used to create a data warehouse that is simply a clone of the source database. Instead of spending months designing and data mapping a data warehouse into a structure that simplifies the accessibility of information for reporting users, the source schema is recreated in the target warehouse database. This approach requires an abstraction layer when the source application’s database schema is too complex for business users to understand. This can be accomplished by creating views on top of the mirrored tables.  Most reporting products allow an administrator to define metadata in the reporting application. This resolves the complexity into topics, which are subject areas such as customer accounts and contacts, financial transactions, support cases, and inventory. Standard database technologies today either have built-in capabilities or use third-party tools to accomplish data replication.  When it comes to replicating data from databases, there are several basic methods for replicating data: Full Table Replication Replication of full tables copies all data from the source to the destination, including new, updated, and existing data. It is useful when records are regularly hard deleted from a source or if the source does not have unique keys or change timestamps. This method, however, has several drawbacks. Full table replication consumes more processing power and generates more network traffic than copying only changed data. When copying full tables, the cost typically increases as the number of rows copied increases. Change Data Capture The data replication software makes full initial copies of data from the source to the destination, following which the subscriber database is updated whenever data is modified. A more efficient replication method since fewer rows are copied when data is changed. Transactional replication usually occurs in server-to-server environments, in which database logs can be monitored, captured, parsed, streamed to the receiving server, and applied to the receiving database. Change Data Capture rarely works for SaaS applications, since most lack notification mechanisms. Snapshot Replication Snapshot replication replicates data exactly as it appears at any given time. This type of replication does not take into account intervening changes to data. This replication mode is used when changes to data are infrequent, such as initial synchronization between subscribers and publishers. Incremental replication based on timestamps A timestamp-based incremental replication updates only the data that has changed since the previous update. By contrast with full table replication, timestamp-based replication copies fewer rows of data during each update, making it more efficient. Among the limitations of this technique are its inability to replicate or detect hard-deleted data and not being able to update records without unique keys. Data Replication Pitfalls To Avoid The replication of data is a complex technical process. While it is advantageous for decision-making, the benefits may come at a price. Data Inconsistency Concurrent updates in a distributed environment are more complex than those in a centralized environment. Data replicated from different sources at different times can cause some datasets to be out of sync with each other. Data could be temporarily out of sync, last for hours, or become completely out of sync. Administrators should ensure that all replicas are updated consistently. To optimize the replication process, it should be well-thought-out, reviewed, and revised as needed. More Data Means More Storage The same data stored in more than one place consumes more storage space. It’s important to consider this cost when planning a data replication project. Data Movement May Require More processing Power and Network Capacity Reading from distributed sites may be faster than reading from a central location, but writing to databases is slower. Replication updates consume processing power and slow down the network. Improving data and database replication can help manage the increased load. Streamline Your Replication Process with the Right Tool Replication of data has both advantages and disadvantages. A replication process that meets your needs will smooth out any bumps in the road. Yes, you can write code internally to handle the replication process – but is this really a good idea? You’re essentially adding another application to maintain, which can be time-consuming and energy-consuming. In addition, some complexities arise from maintaining a system over time: error logging, alerting, job monitoring, autoscaling, and refactoring code when APIs change. By accounting for all of these functions, data replication tools streamline the process. Simplify Data Replication the Right Way With Sesame Software, you can spend more time driving insights from your data and less time managing it. In minutes, Sesame Software can replicate data from SaaS applications and transactional databases to your data warehouse. Once there, you can use data analysis tools to surface business intelligence.  You don’t have to write your own data replication process when using Sesame Software’s click-and-go solution. No coding, data mapping, or modeling is required. Sesame Software ensures the fastest possible data movement with its patented multithreaded technology.  Start gaining data-driven insights within minutes by registering for a free trial !

Learning how to streamline data integrations is imperative to achieving accelerated reporting and analytics for today’s businesses. But let’s admit it – the process of preparing data for reporting is anything but simple.  Making Sense of Data Modern businesses understand the importance of transforming their company’s ever-growing data into key insights. Allowing for a clear picture of how their business is operating, identify trends, and make business-critical decisions. But collecting data from different data sources and connecting that data with where it needs to go can be a complicated and time-consuming process! Additionally, operating in today’s fast-moving and ever-changing business world means you need a system that ensures your data is up-to-the-minute, for accurate analytics. All of Your Data, All in One Place Sesame Software’s data management suite streamlines your data integrations. So you can skip straight to your business insights. Sesame Software starts by building a  fully automated data warehouse  that replicates a mirror image copy of all of your data from different sources. With your data centralized in one location,  high-volume data connectors  quickly and efficiently take your data where you need it. For example, into the BI tool of your choice for accelerated reporting and analytics. Fresh Data = Fresh Insights Sesame Software automatically synchronizes your data throughout the day, every day. This ensures that your data is always up-to-date so that you can trust that your reports are relevant and accurate. With Sesame Software’s rapid deployment and zero data mapping, modeling, or maintenance, you can put your resources into your insights. Customer Success Story Continental Batteries , a reliable battery distributor since 1932, turned to Sesame Software to integrate a substantial amount of NetSuite data to Oracle Autonomous Data Warehouse (ADW). This data would be used with Oracle Analytics Cloud (OAC) for critical business intelligence.  Within just minutes, Sesame Software built a fully automated data warehouse that replicated a mirror image copy of Continental’s NetSuite data. Then, Sesame Software’s high-volume data connectors enabled large volume data migration and integration from NetSuite to ADW. With Sesame Software’s rapid deployment, Continental Batteries was able to free up their time to focus on business insights. With continuous automated data synchronization, the Continental team could rely on their data being up-to-date, for accurate reporting and analytics.  For a deeper look at how Sesame Software helped Continental Batteries achieve their data goals,  read the case study.  If you would like information on how Sesame Software can help your organization,  request a demo.  You can also contact the Sesame Software Team directly at (408) 550-7999. See the Continental Batteries Case Study press release.

ln transformation, new applications are being adopted within organizations at a rapid pace. There is a growing shift from on-premise applications to cloud or hybrid with both cloud and on-premise components, causing significant challenges for organizations to build an integrated enterprise.  An organization that has procured or migrated to Oracle Cloud, encounters various challenges integrating a wide range of Oracle Fusion Application suite with external on-premise and cloud applications. Oracle Fusion Applications Oracle Fusion Applications is a suite of modular Oracle applications that empower customers to manage large, complex business tasks, processes, and workflows in one place. Additionally, this allows for a comprehensive view across HR, supply chain, and financial branches, users are able to achieve greater insight into their businesses, empowering them to make better business-critical decisions. Oracle HCM, Oracle SCM, and ERP Financial Cloud Oracle Human Capital Management, or HCM, enables users to manage a wide range of people processes within one common data source. However, SCM, or Oracle Supply Chain Management, enables users to easily see the complete picture of their company’s finances and operations. Oracle’s ERP Financial Cloud empowers customers to manage all aspects of their business financial modalities in one place. As a result, users can get the in-depth insights needed to make informed decisions. Seamless Integration for Oracle Fusion Sesame Software’s Data Management Suite supports Oracle Fusion Applications – including HCM, SCM, and ERP Financial Cloud – allowing for seamless integration of Oracle Fusion Applications with external, on-premise, and cloud applications. This enables Oracle customers to rapidly replicate and seamlessly integrate Cloud data with Oracle’s Fusion software, providing users with in-depth insights that can quickly maximize their Oracle investment. Sesame Software’s  data connectors  for  Oracle Fusion Applications  streamline integration processes, ensuring data consistency, enhanced performance, and rapid insights. By using patented, scalable technology,  Sesame Software Data Warehouse Builder  enables you to create an on-premise or Cloud data warehouse in just minutes that links all of your integrations to one trusted source. As a result, all of your data from disparate applications and databases are now unified in one high-performance data warehouse. Sesame Software eliminates lengthy warehouse build projects. Just turn it on, configure the login information, and your warehouse is built and loaded! There is no need to hire third-party consultants – saving you significant time and money. Sesame Software Key Benefits Data connectors support replication and integration for a wide variety of cloud and on-premise applications and databases Fully automated data warehouse enables rapid connection to Fusion Applications and other data sources Patented technology ensures the fastest possible data movement Reduces wait time for data handling and enables intelligent automation Lowers total cost of ownership by eliminating data modeling, database design, and data mapping – just click and load Rapid deployment shortens your time to production – get data moving in minutes, not weeks or months! In conclusion, Sesame Software’s high-volume data connectors and automated data warehouse simplify the process of building data pipelines with Fusion applications so that you can free up your time to focus on critical business decisions. Want to learn more?  Request a demo  today!

As companies scale, their data increases and that data often resides in a multitude of separate data sources. Additionally, information from different sources often needs to be combined for operational actions, reporting, and analytical needs. How do you connect, correlate, and analyze these varied outputs and effectively use your data to turn insights into new solutions that increase revenue streams? Oracle Customers are using  Oracle Cloud Infrastructure  to build a lakehouse that provides an efficient platform for integrating all your data—whether in a data warehouse, data lake, or application output—and adds analytics capabilities, machine learning, and AI services to help you get the most value out of your data. Building a lakehouse on OCI can help you overcome the obstacles posed by too much data, too many formats, too many sources and make sense of it all. Data Lakehouse Architecture  Data Lake A data lake enables an enterprise to store all of its data in a cost-effective, elastic environment. This is done while providing the necessary processing, persistence, and analytic services to discover new business insights. A data lake stores and curates structured and unstructured data. Additionally, it provides methods for organizing large volumes of highly diverse data from multiple sources.  Data Warehouse With a data warehouse, you perform data transformation and cleansing before committing the data to the warehouse. With a data lake, you ingest data quickly and prepare it on the fly as people access it. A data lake supports operational reporting and business monitoring. This requires immediate access to data and flexible analysis to understand what is happening while it is happening. Data Lakehouse  Data Lakehouse  architecture combines the abilities of a data lake and a data warehouse to provide a modern data management platform that processes streaming data and other types of data from a broad range of enterprise data resources. Use this architecture to leverage the data for business analysis, machine learning, and data services. Ready to Move Data to Your Lakehouse? It Takes the Right Partner! Oracle customers are looking to leverage the latest technology and data insights to become more innovative to grow and move their business forward. They want to use data science to uncover new business models, trends, and opportunities that support business innovation. Others are looking to become more efficient with their operations and get predictable operating expenses, so they turn to technology to help reduce costs and to boost productivity.  Sesame Software is the top data ingestion tool within the Oracle Lakehouse Partner Ecosystem, allowing customers to bring data from various sources to the lakehouse in just minutes. Data teams are provided with full data visibility and accelerated insights and will no longer need to rely on limited data or their instinct for critical business decisions.  With multi-threaded data ingestion and automated data syncs, your data moves at the fastest possible speed and is continuously updated. Thus, this removes barriers such as manual processes that slow down the business. Customers can leverage automation to help reduce costs and eliminate complexity. Sesame Software Benefits: Rapid data ingestion from various sources to a data warehouse, data lake, or both – the Data Lakehouse Time-saving automation eliminates costly data maintenance – freeing up time and valuable resources to focus on insights  Seamless code-free data flow between data warehouse and data lake  Sesame Software’s enterprise SQL-based solutions have been tested by data scientists and verified on the Oracle Cloud Infrastructure. Customers can move their data with confidence as Sesame Software is supported by the  Oracle Cloud Infrastructure SLA guarantee . This achievement ensures they have full access and control over their cloud infrastructure services as well as consistent performance. Sesame Software can be directly installed from the  Oracle Cloud Marketplace  into your OCI tenancy and can be purchased with Oracle Universal Credits to make the entire transaction as seamless as possible.   Leverage Your Oracle Data Lakehouse Immediately Interested in a fast, code-free data flow that can help your company take advantage of Oracle Data Lakehouse benefits in less than an hour?  Schedule a free demo  of Sesame Software’s data platform and get started today! Sesame Software can also be deployed directly from the  Oracle Cloud Marketplace .  Learn more about Sesame Software and Oracle’s  partnership .

Latest Version of Relational Junction Data Management Suite, Extends Support to New SaaS Applications with Enhanced Metadata Repository and Data Movement Techniques SANTA CLARA, Calif., Sep.23, 2021 /PRNewswire/ —  Sesame Software , the innovative leader in Enterprise Data Management, today announced the rollout of Relational Junction 6.2, the latest version of its suite of data management and replication tools, giving companies the ability to effortlessly create data warehouses for any database or API-enabled application.  Relational Junction  has also added support for many data warehouse platforms, including Oracle Autonomous Data Warehouse, Snowflake, Google BigQuery, and Redshift, using native bulk loaders when appropriate for exceptional performance. Why is this important? Relational Junction’s entire focus is on putting all your data into an instant, fully automated data warehouse that gives you complete control of your data. With only minutes of configuration, customers can automatically build a schema and efficiently move data for business intelligence, analytics, and integration.  “This release has evolved from its predecessor suite of products into a single product that builds on-demand data warehouses out of any database or API-enabled SaaS application with no code, no design, no data mapping, and lights-out continuous operation,” says Rick Banister, founder and CEO of Sesame Software.  Security is at the heart of the product architecture. Sesame Software does not host your data or the product, eliminating all security concerns about vendors potentially allowing data breaches. Instead, Relational Junction can be installed on any private cloud or on-premise hardware platform. Want to use AWS, Oracle OCI, Google Cloud, or Azure? Or just drop it onto your laptop? Sesame Software can support you. UNIX or Windows? No problem. “By integrating data from external and internal sources with Relational Junction, organizations end up with a relational database that’s fully secure and optimized for their specific needs,” says Banister.  “This gives every data-driven company real-time 360-degree access to their most important data, ensuring that sales, marketing, and the C-Suite are aligned for day-to-day decision making and long-term strategic planning.” To learn more about how Relational Junction 6.2 allows you to quickly and securely flow data across any data source or application in your tech stack, request a  demo  today. About Sesame Software Headquartered in Santa Clara, California, Sesame Software is the Enterprise Data Management leader, delivering data rapidly for enhanced reporting and analytics. Sesame Software’s patented Relational Junction suite offers superior solutions for data warehousing, integration, as well as backup, and compliance to fit your business needs. Quickly connect to SaaS, on-premise, and cloud applications for accelerated insights. To learn more, go to  www.sesamesoftware.com .

Sesame Software Provides Rapid Integration with NetSuite and Oracle Autonomous Data Warehouse, Enabling Enhanced Business Intelligence Using Oracle Analytics Cloud Sesame Software’s Data Management Suite provides rapid integration with NetSuite, Oracle Autonomous Data Warehouse (ADW), and more than 100+ other cloud applications and databases. All connected data sources can be moved into any analytical database (such as Autonomous Data Warehouse) to be used by Oracle Analytics Cloud for instant access to your business data. After just a few minutes of setup, Sesame Software replicates all your applications and databases into a high-performance ADW data warehouse, enabling robust business intelligence. As new data becomes available, Sesame Software updates your data warehouse, so your data is always fresh. What is NetSuite? NetSuite is the world’s leading provider of cloud-based business management software with multiple options to connect to your data. Thus, NetSuite helps companies manage core business processes with a single, fully integrated system covering ERP/financials, CRM, eCommerce, inventory, and more. What is Oracle Autonomous Data Warehouse? Powered by Oracle Database, Oracle Autonomous Data Warehouse provides unbeatable performance. Built-in adaptive machine learning eliminates manual labor for administrative management. What is Oracle Analytics Cloud? Oracle Analytics Cloud empowers business analysts and consumers with modern, AI-powered, self-service analytics capabilities. In addition, the capability also allows for data preparation, visualization, enterprise reporting, augmented analysis, and natural language processing. Why Store NetSuite Data into Autonomous Data Warehouse (ADW)? Deeper Analytical Insights Extracting the data into ADW allows the data to be utilized by powerful BI tools, such as Oracle Analytics Cloud, in a deeper, meaningful manner Retain Historical Data Storing data in ADW ensures records are kept intact, allowing for historical trends, which is a feature only possible with a data warehouse solution Versioned records provide a complete audit trail of all changes Complete 360 View of Corporate Data Using one data warehouse to view many sources Providing a complete data model of your entire company Challenges of Accessing Your Data in NetSuite Direct access to NetSuite data is not possible The SuiteTalk and SuiteAnalytics components of the SuiteCloud framework enable integrations of NetSuite with other on-premises or cloud solutions While SuiteTalk provides the ability to access NetSuite data and business processes through an XML-based API, it requires skills such as Microsoft .NET or Java to build integrations Not practical for non-developer users Alternative methods exist for extracting the NetSuite data, such as Sesame Software. How Sesame Software Extends NetSuite Sesame Software’s Data Warehouse Builder provides an instant data warehouse for NetSuite. Not to mention this enables scalable data integration that improves the reliability and performance of reporting and analytics. Create a local data warehouse that is a mirror of your NetSuite data. Fault-tolerant architecture that helps you get your NetSuite data consistently to your data warehouse and preferred BI tool with zero data loss. With our patented technology, Sesame Software speeds up your implementation time, making your data warehouse actionable within minutes. By integrating data from external and internal sources, organizations are provided with a database that gives 360-degree access to their most important data. Thus, data teams will be able to make day-to-day decisions and plan long-term without any issues. Ease of Use NetSuite data is extracted directly into ADW by configuring credentials for NetSuite and the ADW database. Not to mention, there is no data modeling required. The database will automatically create custom fields. Also, one job request can also retrieve all NetSuite data. Fastest Possible Data Integration Sesame Software is the quickest solution for loading NetSuite data into ADW. In addition, Sesame has patented algorithms to extract large datasets by time-slicing the data. XML record data is queried efficiently with Sesame Software by using views for lists. Sesame Software uses views for lists Sesame Software also supports saved searches. Connect to ADW Now Using Sesame Software Sesame Software is  ADW certified , and you can learn more on how to create a connection to Oracle Autonomous Data Warehouse using Sesame Software . Watch the Oracle-sponsored demo video that shows how simple it is to move data from NetSuite to ADW using Sesame Software, and how you can instantly access OAC for advanced analytics. Additionally, Oracle has recognized Sesame Software for bringing “ground-breaking” solutions to the Oracle Cloud Marketplace to support critical data management and analytics use cases. Sesame Software was highlighted as one of the key innovative solutions that integrates well with Oracle Cloud Infrastructure’s high-performing compute, storage, and database services to power Big Data and analytics projects. Want to Learn More? You can  request a demo  with one of our data experts or check us out on the  Oracle Cloud Marketplace !

The physical design of your database optimizes performance while ensuring data integrity by avoiding unnecessary data redundancies . The task of building the physical design is a job that truly never ends. As a result, you need to continually monitor the performance and data integrity as time passes. Many factors necessitate periodic refinements to the physical design. In this article, we will discuss the concept of how physical structures of databases affect performance, including specific examples, guidelines, as well as best and worst practices. Physical Database Design Process Physical database design is the process of transforming a data model into the physical data structure of a particular database management system (DBMS). Normally, Physical Design is accomplished in multiple steps, which include expanding a business model into a fully attributed model (FAM) and then transforming the fully attributed model into a physical design model. Conceptual, Logical, and Physical Data Models You begin with a summary-level business data model that’s most often used on strategic data projects. It typically describes an entire enterprise, which allows you to understand at a high level the different entities in your data and how they relate to one another. Due to its highly abstract nature, it may be referred to as a conceptual model. Common characteristics of a conceptual data model: A conceptual data model identifies important entities and the high-level relationships among them. This means no attribute or primary key is specified. Moreover, complexity increases as you expand from a conceptual data model. A logical data model, otherwise known as a fully attributed data model, allows you to understand the details of your data without worrying about how the data will be implemented in the database. Additionally, a logical data model will normally be derived from and or linked back to objects in a conceptual data model. It is independent of DBMS, technology, data storage or organizational constraints. Common characteristics of a logical data model: Unlike the conceptual model, a logical model includes all entities and relationships among them. Additionally, all attributes, the primary key, and foreign keys (keys identifying the relationship between different entities) are specified. As a result, normalization occurs at this level.  The steps for designing the logical data model are as follows: First, specify primary keys for all entities. Then find the relationships between different entities. Find all attributes for each entity. Lastly, resolve many-to-many relationships. Finally, the physical data model will show you exactly how to implement your data model in the database of choice. This model shows all table structures, including column name, column data type, column constraints, primary key, foreign key, and relationships between tables. Correspondingly, the target implementation technology may be a relational DBMS, an XML document, a spreadsheet, or any other data implementation option. Common characteristics of a physical data model: Describes data requirements for a single project or application. Specifies all tables and columns. Contains foreign keys used to identify relationships between tables. Physical considerations may cause the physical data model to be different from the logical data model. The physical design is where you translate schemas into actual database structures. After that, you transform the entities into tables, instances into rows, and attributes into columns. At this time, you have to map: Entity to Table Attribute to Column Primary Key and Alternate Key to Unique Index Index to Non-unique Index Foreign Keys to Non-unique Index Transformation Choosing a physical data structure for the data constructs in the data model. Optionally choosing DBMS options for the existence constraints in the data model. Does not change the business meaning of the data. First transformation of a data model should be a “one-to-one” transformation. Should not denormalize the data unless required for performance. Based on shop design standards and DBA experience & biases. Entity Subsetting – Choosing to transform only a subset of the attributes in an entity. Dependent Encasement – Collapsing a dependent entity into its parent to form a repeating group of attributes or collapsing a dependent entity into its parent to form a new set of attributes. Category Encasement Category Discriminator Collapse Horizontal Split Vertical Split Data Migration Synthetic Keys – The merging of similar entities using a made-up key. Always loses business vocabulary and is never more than BCNF. Also used a lot in packages to make them easily extendable. Adding Summaries – Adding new entities that are each a summary of data upon a single level of a dimension. Adding Dimensions Moreover, it is often necessary to apply multiple transforms to a single entity to get the desired physical performance characteristics. All physical design transformations are compromises. Database Definition Language (DDL) Except for data cubes, a one-to-one translation of the Physical Model into the specific DDL of the database: Entity to Table Attribute to Column Primary Key and Alternate Key to Unique Index Index to Non-unique Index Foreign Keys to Non-unique Index  Data Cubes Data cubes are the exception to the DDL one-to-one translation because: Proprietary physical databases Have their own structure definition language Have their own data loaders Physical Database Design: Watson-Watt’s Law of Third Best The best never comes. Second Best takes too long. Identify the Third Best – the design that can be validated in time to meet an identified. In conclusion, physical database design is no easy feat. If you feel intimidated by it, you’re not the only one. Contact a member of our team for more information!

Migrating your workloads to any cloud infrastructure service will provide scalability, flexibility, and reduce your company’s costs. It will also allow IT to focus on key business initiatives and strategic projects. When considering moving your various application data from on-premises to Oracle Cloud , you will want to assess what data needs to be migrated to the cloud and select an appropriate data transfer method. Benefits of Moving to Oracle Cloud Performance Increased flexibility and reliability when you run your applications on Oracle Cloud High performance at a lower cost than deployments running on-premises or other cloud infrastructures Shared infrastructure that empowers your entire business to run faster and scale up (or down) to meet peak compute demands with ease Security Enterprise-grade security at every level of the stack, ensuring user isolation, and data encryption at every stage of the life cycle Fine-tuned security controls, compliance, and visibility through comprehensive log data and monitoring solutions Personalized Solutions and Cost Savings Comprehensive database migration services, so there will be one that exactly matches your requirements Hardware cost savings, increased business flexibility, and greater efficiencies in the short- and long-term In essence, the flexibility of Oracle Cloud provides the scalable infrastructure, development capabilities, hardware, and software options that support your business. This empowers you to create and utilize the agile innovation to keep your company competitive for years to come. It Takes the Right Partner If you’re ready to improve your competitive edge by increasing your company’s agility, you’re ready to move to Oracle Cloud. Cloud solutions enable your company to run your business the way you want, at the cost and performance you prefer. However, to access the full benefits of the cloud, you need a partner to help you accelerate your cloud journey! Sesame Software’s Data Management Platform, Sesame Software, can migrate your application data to Oracle Cloud Infrastructure with ease. There is no need for data modeling, mapping, or coding. With simple click-and-load deployment, you can have your data moving at the fastest speed possible. Additionally, Sesame Software manages your workload migration. This ensures cost savings, time savings, and increased efficiency and agility—without having to rearchitect your solutions! Learn more about Sesame Software and Oracle’s Partnership today. Sesame Software can be purchased directly via Oracle Cloud Marketplace . Take Advantage of Oracle Cloud Benefits Immediately Interested in a fast, error-free application migration that can help your company take advantage of Oracle Cloud benefits in less than an hour? Schedule a free demo today !