What’s New in Centerprise 7.5

New additions, enhanced performance, and improved usability – Centerprise 7.5 is better than ever!

Centerprise Data Integrator is an end-to-end ETL and data integration solution that empowers business users to perform complex data integration tasks without relying on IT. With the code-free, drag-and-drop IDE of Centerprise, we aim to flatten the learning curve for business users and make self-service data integration possible for them.

Here is everything that we’ve improved and added in Centerprise 7.5 to make it even better and a feature-rich data integration solution.

Improved server resiliency

We have greatly improved the server resiliency in Centerprise 7.5. This will ensure trouble-free server operations, better and quick recovery in the case of a database outage event, and overall improvement in the performance.

Here are some of the benefits that the users will get with this improvement:

  • Astera integration server will no longer go in a permanent state of error in case of a database outage event and will automatically recover as soon as the connection is restored.
  • Server connection will be automatically established when the connection to the repository database is available and no manual restarts will be required.
  • Activity and error tracking are greatly improved in the new release. The server writes database connection issues in the Windows event log and includes a link to the error file for easier troubleshooting.

New transformations added to the library

The transformation library in Centerprise Data Integrator is extensive and encompasses a wide range of data integration scenarios. However, we strive on making the ETL and data integration process easier for our users and thus we have included new transformations in Centerprise 7.5. These new additions are:

Switch transformation

Switch data representation to get accurate business insights

There are more than one ways of representing the same information. It is important to have the information presented in the correct representation to drive accurate business insights. Switch transformation is used to replace the data in the source file with any other representation that is more relevant to the present business case.

Learn more about how Switch transformation works in Centerprise and how it can be applied in a business scenario.

Reconcile transformation

Reconcile information for self-service data analysis

Reconcile transformation helps in reconciling information in the source data. Application of the transformation can be seen in a wide range of business use cases where merely storing data is not sufficient and monitoring the changes in data over time is essential to drive critical business decisions.

Some of the benefits of the Reconcile transformation are:

  • Can be applied to flat or hierarchical source data.
  • The transformation can be applied to inner nodes as well when the source data is in a hierarchical format.
  • Business users can perform data analysis by comparing old and new values for specific fields in the source data.
  • Provides options to view data in multiple layouts so users can choose the layout which is most in line with business requirements.

Read more about how Reconcile transformation works in Centerprise here.

Merge transformation

Merge data from disparate sources for a consolidated and unified view

As the data collection avenues are increasing, it has become a common practice for businesses to integrate data from multiple disparate sources. In some cases, the same data is contained in different sources and businesses face the need to merge the fragmented data for a consolidated view. Merge transformation in Centerprise helps in achieving this task.

What’s Next?

We have a number of new features lined up for the upcoming releases and we are excited to share the updates with you. Subscribe to our blog or join our community of data professionals to stay informed. To see Centerprise in action, schedule a discovery call with us today!

Switch Transformation in Centerprise 7.5

Overview

Switch transformation is among the newly introduced transformations that are included in Centerprise 7.5 version. It is a basic, yet an extremely useful transformation and has applications in a wide range of business use cases.

This transformation can be applied when there is a need to replace information stored in the source data with some other relevant information. Information is switched (or replaced) based on some preassigned rules.

Switch Transformation Explained with a Business Use Case

An organization maintains a database for their employees. Employee_Info table in that database contains information such as employee’s name, department, salary and employee ID.

Figure 1: Source file and data preview before the switch transformation is applied

For the ease of maintenance, the organization assigned a specific number to each department (Marketing, Sales, Finance etc.) and that information is recorded in the Department field as shown in figure 1.

Using the Switch transformation, the numeric values stored in the Department field can be easily replaced with their corresponding department names.

Basic Mode in Switch Transformation

The basic mode in Switch transformation works like a List Lookup. In this example, we want to replace the numeric information stored in the Department field with their actual corresponding departments.

Figure 2: Switch Transformation (Top: Basic Mode – Bottom: Enhanced Mode)

Drag-and-drop the transformation from the toolbox to the designer and map the required fields. Here we are mapping EmployeeID, Department, and Salary fields from the source file to the Switch transformation (BasicMode_Switch).

In the properties window of the transformation, select the field that you want to perform the switch operation on, and specify the criteria in Case Value, based on which Centerprise will look for the information and replace it with the given information in the corresponding Output Value.

Figure 3: Switch transformation properties window (Basic mode)

Now if you preview the output or write the transformed data to any destination, you will see that the numbers in the Department field are replaced with the information that is based on the rules assigned in the properties window shown in figure 3. In a case where Centerprise won’t find any matching case value, it will automatically assign the specified default output value, which is Admin in this example.

Figure 4: Data preview after the Switch transformation is applied

Enhanced Mode in Switch Transformation

The Enhanced mode in the Switch transformation provides more flexibility to the feature because it enables users to set lookup criteria by writing expressions. Figure 5 shows the properties window of the enhanced mode in Switch transformation.

Figure 5: Switch transformation properties window (Enhanced Mode)

Suppose our fictitious organization has set criteria for issuing credit cards. Employees whose salary is less than $3,500 are not eligible for the credit card. Employees with a salary between $3,500 and $5,000 are eligible for the Silver card. Other similar assignments can be seen in the Case Expression column in figure 5.

The organization has the data about employee’s salaries and the ranges for the issuance of credit cards. In this case, employee’s salary, which is the information that is stored in the database, is not relevant to the concerned department. What they’re looking for is to identify which employee is eligible for which perk. This can easily be achieved by matching the salary ranges defined in the Case Expression and switching it with the corresponding information about the credit cards specified in the Output Expression.

Figure 6 shows the output preview for the enhanced mode case in the Switch transformation.

Figure 6: Output preview of switch transformation in the enhanced mode

Benefits of the Switch Transformation

Switch transformation gives users more control over data as it enables them to transform the given information into something more relevant, which is best suited to the given business scenario. When applied to any data set, switch transformation matches source data for the criteria specified by the user, and replaces the information with the desired output (also specified in the layout) wherever the criteria is met.

5 Best Practices to Help You Reap the Benefits of Data Democratization

Born out of the necessity to leverage more value out of Big Data, data democratization is an approach that allows data to pass from the hands of a few data analysts into the hands of business users.

With a data democratization strategy in place, every user in the organization, regardless of their technical prowess, can have access to data for timely and more insightful decision-making. This, in turn, helps analysts spend more time using data and less time finding it. However, even when an organization wants to empower every employee with easy access to data, there can be several infrastructure, culture, and governance-related impediments to making data available freely to them.

In this blog, we have listed 5 best practices that a business can adopt to overcome these challenges and establish enterprise-wide data democracy.

1. Gain an Understanding of the Entire Data Ecosystem

As an organization grows, so do the volume, variety, and velocity of incoming data and the challenges associated with managing it. Information becomes siloed in systems and is accessible by relevant teams only, thereby offering a myopic vision of the data space to users.

In-depth understanding of the data ecosystem and the fragmented systems that comprise it is integral to designing an integrated data space that offers all the users a holistic view of the information assets, along with the metadata and context they need to feel more confident about the relevance and trustworthiness of data.

2. Make Data Available to Everyone

In most organizations, data integration and analysis tools sit with IT departments who act as the gatekeepers of data, with business users at the mercy of data scientists to gain access to relevant data for BI and analytics. This can result in a data management process that is slow, frictional, and highly IT-reliant.

For businesses that wish to benefit from data democratization, it is essential to invest in data integration and analysis tools that offer the same usability and performance to everyone from developers to the end-user with limited technical knowledge.

3. Tame Your Legacy Data

Data democratization is not just about making fresh data accessible for analysis and reporting. It also involves liberating the data trapped within legacy systems for answering questions that were not contemplated by people who originally collected this information.

However, legacy systems are inherently inflexible and can hamper the data democratization efforts of any organization. To overcome the challenge and integrate legacy data into modern infrastructures, businesses must invest in data integration tools that offer instant API connectivity to not only popular databases but also cloud-based systems and applications to ensure interoperability.

4. Empower Users with Self-Service Analytics

For organizations to reap the full benefits of data democratization, they must empower their users to not only access data but also make data analysis and reporting part of day-to-day operations.

Although data integration and BI tools and technologies have evolved greatly over the past few years, finding a data management platform that facilitates access, analysis, and reporting of data in highly consumable ways remains an ongoing quest for most enterprises.

The solution to the problem lies in finding a data integration solution that lets you take advantage of the data the resides in previously disconnected systems, offers out-of-the-box connectivity to BI and analytics tools, as well as allows employees without technical knowledge to easily manipulate and analyze data.

5. Train Employees on How to Best Use Data

Data governance go hand in hand with data democratization, and lack of a data governance plan can quickly result in information overload, poor decisions, and reputational risk. To avoid these demerits of data democratization, everyone in the organization should be trained on how to best use the data, importance of understanding data lineage, and how it can be transformed for BI and analytics.

By taking these steps to democratize data, you can dramatically increase the value your business extracts from information assets and make data your basis of competitive differentiation.

data democratization infographics download

Press Release: Astera Software Partners with Tableau to Bridge the Data-to-Insight Gap

Tableau’s Integration with Astera’s Centerprise Data Integrator Enables Users to Utilize a Complete Suite of Data Integration and Self-Service Analytics Capabilities through a Single Platform.

Astera Logo     

Astera Software, a fast-growing company that develops enterprise-grade data management solutions, has joined hands with the leading visual analytics platform, Tableau. This partnership is geared towards enabling business users to leverage an end-to-end data management and visualization platform that offers advance-level capabilities for data extraction, profiling, integration, warehousing, and visualization.

Data integration and reporting & analyses are two sides of the same coin that enable businesses to get a holistic view of their operations. The integration of Astera’s Centerprise Data Integrator with Tableau will allow users to benefit from the capabilities of both solutions, thereby amplifying their BI efforts, while improving data quality and minimizing the complexities associated with data integration and analysis.

Elucidating the idea behind Tableau and Centerprise Data Integrator integration, Jay Mishra, COO Astera, said, “Nowadays, data is the core business driver, providing a solid foundation for decision makers to base their strategic decisions. However, the biggest challenge is making sense out of mountains of data pouring in from a myriad of sources. Centerprise Data Integrator simplifies the complex data integration processes, while Tableau allows business users to perform analyses on interactive dashboards in real-time to get meaningful insights. With this integration, business users can enjoy best of both worlds.”

“We are excited to welcome Astera Software as one of our technology partners,” said Todd Talkington, Director of Technology Partnerships at Tableau. “Astera’s vision of enabling enterprises to automate the process of data extraction, integration, and warehousing, combined with Tableau’s visual analytics platform, will enhance the decision-making capabilities of our customers and help them extract more value out of their data.”

Centerprise Data Integrator uses an industrial-grade ETL engine designed to power data integration processes in an intuitive, code-free environment. It assists business users in all stages of integrating and transforming unstructured and structured data into meaningful information, ready to be fed into BI and self-service analytics tools.

Tableau is a powerful visual analytics platform that enables users to build dashboards and perform real-time analysis to extract actionable insights. With Centeprise’s process orchestration and job scheduling capabilities, business users can design workflows to transform incoming data, integrate it with Tableau, and acquire reports and track key performance indicators on Tableau’s flexible and intuitive dashboards with just a few clicks.

About Astera Software

Astera Software develops powerful, intuitive data management solutions focused on eliminating the complexities in data extraction, integration, and warehousing processes. Astera’s solutions are consistently acclaimed for their superior usability, intuitive interface, and high performance, ensuring a rapid ROI and scalability to meet the most demanding data management tasks. To see how Centerprise Data Integrator can help you bring data from disparate sources to a single place for analyses and reporting, view the product demo or download a free trial.

For additional information and regular updates, visit the company’s  FacebookTwitter, or LinkedIn page.

Contact:

Website URL:  http://www.astera.com

Email: sales@astera.com

Address:  Astera Software, 310 N Westlake Blvd, #140 Westlake Village, CA 91362

Phone: 888-77-Astera

 

 

Server Resiliency Improved in Centerprise 7.5

We are releasing Centerprise Data Integrator 7.5 very soon, and as with every new release, our focus is to improve the product experience for our customers. We have added new features and have made improvements to the existing ones in the upcoming build.

One of the key changes we have made in the 7.5 version is greatly improving the server resilience. This will ensure trouble-free server operations, even when connection issues occur between Astera server and the MSSQL server hosting the repository databases. Enhanced server resilience will not only improve the overall performance of Centerprise, but will also prove to be significantly beneficial in the scenarios where 24/7 operation with high uptime is required, which is the case with most of our enterprise customers.

Key Benefits of Improved Server Resilience

With improved server resilience, users will get the following benefits:

Better and quick recovery of the server

With improved server resilience, Astera server will no longer enter a permanent error state after a database outage event. Instead, it will recover as soon as the connection is restored, allowing the customer to continue with the normal operations without any downtime. The server can now survive most repository database outages without any user action required.

Auto-recovery mode – no manual restarts required

Manually restarting the server will be no longer necessary in case of lost connectivity. The server will recover automatically. If the server starts up when a connection to the repository database is not available, the server will enter a paused state waiting for the connection. When the connectivity is restored, the server will enter its normal operational state without the need for manually restarting it.

Improved performance

Improved server resilience means improved performance. If the server is running the flows and an outage event occurs, the flows will not be terminated, and instead paused waiting on connection. This means that the server will be able to complete most flows successfully even in the presence of multiple random network and/or database connection issues.

Activity/Error tracking

Logging in general, and logging of database connection issues in particular, have been greatly improved in the new release. Moreover, the server writes database connection issues in the Windows event log and includes a link to the Error file for easier troubleshooting. An entry is also added to trace the server log when the connection is restored.

 

TDWI Anaheim Conference 2018: Post Event Highlights

TDWI Anaheim Astera DWAccelerator

TDWI Anaheim Conference 2018 has been a truly transformative journey for Team Astera. We attended several talking sessions, had one-on-one meetings with thought leaders and industry experts, and received a positive response for our products, especially our end-to-end data warehouse automation solution, DWAccelerator.

Event Overview

TDWI Anaheim Astera DWAccelerator

TDWI is the focal point of education, research, and insights when it comes to data management, analytics, and the latest trends and technologies within the big data realm. TDWI Anaheim Conference 2018 was held at the happiest place on Earth, Disneyland® Hotel, from August 5 to August 10, inviting data professionals from a whole host of renowned companies from across North America.

The 5-day long conference featured over 65 half- and full-day sessions by TDWI instructors and industry experts. It was geared towards providing hands-on training and practical experiences to data professionals and aspiring individuals. The scope of the conference revolved around four major areas, including data infrastructure and technologies, modern data management, machine learning and advanced analytics, and data strategy and leadership.

Highlights of the Event

TDWI Anaheim Conference 2018 enabled us to explore new avenues and learn about the latest happenings in the data management industry. Here is our version of the conference:

Our CEO Shared Data Warehousing Automation Insights

TDWI Anaheim Astera DWAccelerator

Ibrahim Surani, CEO Astera Software, conducted a talking session on ‘Model-Driven Data Warehouse Development.’ He started with the importance of data and highlighted major challenges businesses generally face when executing a data warehousing or integration project. He shed light on the indispensability of data warehouse automation in enhancing the quality, speed, and accuracy of data warehouse performance. He further talked about the ingredients for achieving data warehouse automation, which included source data modeler, dimension modeler, robust ETL engine, seamless connectivity, and high-performance ETL engine.

The crux of the talk was the meta-data driven model, which he explained in the form of a 4-step process, each illustrating the key aspects of automating the data warehouse development process. Ibrahim emphasized on the benefits of model-driven data warehouse automation, allowing businesses to cut maintenance costs, reduce time to market, and minimize handoffs between users and software tools, without compromising on the flexibility and power of the solution.

Discussions with Industry Experts on DWAccelerator

TDWI gathers industry experts and thought leaders from renowned research firms and Fortune 500 companies, some of which are instructors for the platform as well. We did several exclusive product demos and received positive feedback for DWAccelerator’s capabilities.

One of the TDWI instructors and Managing Research Director at EMA, John L Myers, showed great interest in DWAccelerator’s automation. The product is capable of drastically reducing the time-consuming process of developing enterprise data warehouse architecture and designing ETL processes. He was impressed with DWA’s several features, such as automatic joins, load policy configuration, and flow generation.

Our CEO and COO have been invited to participate in the panel for data warehouse automation in the upcoming TDWI conference in Orlando. Be sure to be on the lookout for the updates regarding our booth and free conference passes for TDWI Orlando Conference.

The Final Words

TDWI has been tracking trends and technologies shaping data and educating companies and professionals how to utilize data to its maximum potential for over 20 years. TDWI Anaheim Conference 2018 has been an insightful experience for our team and proved to be a great platform to connect with renowned industry names and clients looking for automated data warehousing solution, like DWAccelerator.

 

Big Data Toronto 2018: Post-Event Highlights

Team Astera participated in Big Data Toronto 2018 as an exhibitor and had a truly great experience networking with data scientists, practitioners, and thought leaders from across North America and beyond. We took the opportunity to introduce our end-to-end, agile data warehouse solution, DWAccelerator, and received a great response from the attendees.

Event Overview

Going strong in its third year, Big Data Toronto is among the most acclaimed big data and analytics conferences & expos in Canada. Over 4,000 innovators, 60+ exhibiting brands, and 100+ speakers gathered under one roof to embark on an educational journey and exchange technical insights, while providing an outlook on future trends of technology and best practices related to AI and data science.

The conference, held in Metro Toronto Convention Centre, was a two-day event, and covered key topics, including digital transformation, data governance, data management, predictive analytics, advanced machine learning, cyber security and privacy, and more.

Highlights of the Event

Big Data Toronto 2018 has been a thought-provoking and insightful journey for our team. Here is what we learned from the conference:

Agile Data Warehousing Practices

Data warehousing, as a field, stays highly relevant for businesses in the big data era, albeit the expectations have changed significantly over time. Agility in design and development of warehouses has become a key requirement. Businesses are diligently seeking solutions that can accelerate core data warehousing functions, such as data integration, analysis, and cleansing for business intelligence, among others.

Intelligent Data Extraction

Businesses are increasingly taking interest in agile data warehousing solutions that offer effective data extraction from complex structured and unstructured sources, as well as traditional sources. They need accurate, intelligible results quickly to acquire analytical reports through business intelligence tools, allowing them to conform to the changing market dynamics, as well as for making sound decisions for future endeavors.

DWAccelerator – The Solution to Major Data Warehousing Problems

Massive setup costs, time-consuming processes, and lack of agility and flexibility are some of the key obstacles met during BI project implementation. DWAccelerator can very well be the answer to all these issues. DWA is an all-round solution that instills agility and automation in data warehouse designing and development without having the user write a single line of code.

Our COO, Jay Mishra, conducted a session on “Accelerating your Data Warehouse Project” and shed light on problems and ways for businesses to speed up setting up a data warehouse. He presented DWAccelerator and showed that while a data warehouse, using traditional techniques, takes anywhere from a few weeks to several months to build, it can be done in under an hour with our automated solution. Some of the highlighting features of DWAccelerator, such as data models based mapping, automatic field matching, flow generation, and the concept of virtual data models, were well received.

We Met Prospective Partners and Clients

The innovative features and powerful data warehousing capabilities of DWAccelerator gathered much attention from the attendees. We talked to many data professionals and showcased how our product can automate the entire data warehousing process and cut down the costs and time required. This generated a lot of interest in our product, inspiring several new partners and prospective clients to join hands with Astera.

Overall, Big Data Toronto 2018 offered a plethora of insights and concepts that are likely to pave way for future technologies and trends. In addition, the Canada’s #1 big data and AI conference proved to be a great opportunity for us to meet new clients and partners and provide exposure to the latest addition to our product line, DWAccelerator.

PDF-Based Data Extraction Made Easy with ReportMiner

Businesses have used PDF format for exchanging data because of its convenience and reliability. However, manual extraction of data from PDFs is a challenging task. Some of the commonly exchanged PDF documents include purchase orders, invoices, financial statements, and valuation reports. In this blog, we discuss how businesses can liberate important business data from PDFs with automated PDF data extraction.

Challenges of PDF Data Extraction

Many businesses find data extraction from PDF documents challenging as they are in an unstructured format. Previously, businesses relied on the IT department to perform this task, increasing the burden on IT personnel, which led to delays in data exchange.

In most cases, the requirement is to extract data not from only one, but a batch of similarly structured files. In this case, manual extraction of data from PDFs is not only time-consuming but can also lead to errors. A data extraction tool can reduce manual effort required and save time by automating extraction from PDF documents.

Since an organization receives PDF documents in different formats such as scanned PDFs, text-based PDFs, and PDF forms, a desirable data extraction solution should be able to deal with all kinds of PDFs.

How ReportMiner makes PDF-based Data Extraction Painless?

Astera offers a data extraction solution for all PDF-based documents. ReportMiner’s automated data extraction features make it an easy to create and deploy end-to-end integration solution for any use case involving data extraction from PDF sources.

Featuring a user-friendly interface, the solution design is based on a visual, drag-and-drop environment and does not require any form of coding or scripting.

  • Text-based PDFs: ReportMiner can read directly through text-based PDFs and extract the required data based on the designed extraction template.
  • Scanned or Image-only PDFs: Some of the source documents that companies receive are image-only PDFs such as scanned invoices. ReportMiner’s OCR capability creates a text equivalent of images stored in PDF documents. That point onwards, the extraction process is identical to text-based.
  • PDF Forms: In some cases, businesses also deal with PDF Forms to collect important information such as customer details. ReportMiner enables extraction of data from these forms and makes critical business data available for further use.

Crucial business data is often trapped in PDF documents. ReportMiner enables businesses to liberate data from different types of PDFs with its extensive data extraction features. Streamlined PDF data extraction, combined with the ability to automate the process, helps businesses save time and gain access to mission-critical information promptly.

Download our whitepaper, ‘Liberating Data from PDF Documents’ to learn how ReportMiner can help businesses in extracting business data for further processing.

The Dilemma of Build vs Buy – How it applies to Enterprise Software

If Shakespeare was an IT manager, the famous question ‘To be, or not to be’ would have been ‘To build or to Buy’. In fact, the phenomena of DIY-ing something or buying a commercial product is not only limited to enterprise software. IKEA is running a whole business out of providing utility to ‘build proponents’ and DIY enthusiasts. While building furniture can be fun, building an enterprise level software – not so much.

Build vs Buy

Like any other business decision, the decision to either build a software or to buy a commercial product is significantly influenced by total cost of the approach and return on investments. If you’re facing a similar dilemma, the table below summarizes the prospects and consequences of both the approaches.

         Metrics and KPIs                     Build Approach                        Buy Approach
Cost of deployment Hiring a team of developers, designers, programmers to build the solution License fee of the product and deployment costs
Time to market – Time to develop the product

– Time for performing QA analysis

– Time to fix any patches or bugs found

– Time to deploy the solution

– Product development, QA analysis and patch fixes are already taken care of by the solution provider. Therefore, solution can directly be deployed.

– Time to configure and install the product.

Ongoing maintenance and support costs A dedicated team of IT professionals should be on-board to help with ongoing product support and maintenance Updates, maintenance and customer support are handled by the solution provider. However, the solution provider might charge a fee for providing these services
Learning curve A steep learning curve is usually associated with the developed product Commercial products are developed to be used by a wide range of audience with varying levels of technical skills therefore, in most cases these solutions are designed to be more intuitive and user-friendly

 

When is ‘Building’ the right approach?

Building a software is going to be beneficial for your business if:

  • The software is going to give you sustainable competitive advantage
  • No other available solution can meet your business needs
  • The end-points from where your business collects data are not volatile or prone to frequent changes
  • You have substantial resources to cover the costs associated with building and maintaining the software

When is ‘Buying’ the right approach?

You should opt for buying a commercial software if:

  • Building a software is not the core of your business and is not going to yield you any competitive advantage
  • You have limited resources and you would rather invest them in improving your core business activities
  • There are solutions available that address the challenges your business is facing
  • You are looking for a quick solution that can be immediately deployed

IT manager at Brickell Bank, formerly known as Espirito Santo Bank, faced challenges in migrating broker data from MS Access database to IBM mainframe data warehouse. Learn more about the approach he opted for and other factors that influence build vs buy decision by downloading the free white paper.

An Automated Approach to Modeling Your Slowly Changing Dimensions

Business data is inherently susceptible to change with the passage of time and impacts the business in different ways. In data warehouses, the effect of time on our dimensions and facts requires careful study for the repository to meet the business intelligence objective of delivering up-to-date information to decision makers.

Question is, how best to handle these changes?

Developing a dimensional model that captures the different states of your data with respect to time is a key objective of an Enterprise Data Warehouse. For measures in our fact tables, we can use date dimensions and link them using foreign keys. For dimensions, the complexity of handling changes increases greatly. Each step of the Slowly Changing Dimension (SCD) flow must be hand-coded using multiple, complex SQL statements. The implementation is lengthy and complex, and affects the business’ ability to maintain its data quickly and reliably – which is always a critical consideration.

Slowly Changing Dimensions in Centerprise

Compared to the traditional hand-coded approach to the slowly changing dimension flow, Astera offers an automated implementation using a completely drag-and-drop interface. Source data is mapped to an SCD object in Centerprise, which pushes system-generated SQL statements directly to the target data warehouse (Read: Pushdown Optimization Mode in Centerprise) based on the field layouts defined by the user. Each column in the user’s table can be designated as Surrogate Key, Business Key, SCD1, SCD2, etc. (see below) within the component’s properties in Centerprise. The platform handles the update strategy, performance considerations, routing, and complex joins automatically on the backend, as long as the SCD Field Types in below screen are defined correctly.

Field Layout - Slowly Changing Dimensions component
SCD Object Properties in Centerprise

Automating Type 1 & 2 Slowly Changing Dimension Implementation

Centerprise supports both Type 1 and Type 2 SCD to update records with and without maintaining history.

SCD Type 1

This type deals with updates in the dimensional table, for cases when preserving history is not a consideration and you need to replace the old values in your table with recent ones.

To use SCD Type 1 in Centerprise, you can mark your column as ‘SCD1 – Update’ in the Layout Fields menu of the SCD object in Centerprise, as seen in above screenshot for the ‘Contact Title’ column.

SCD Type 2

This type deals with changes in your dimension that need to be tracked. A new record is inserted with each change, and the existing record is marked as expired, by date, version, or status.

To use SCD Type 2 in Centerprise, mark your chosen column as ‘SCD2 – Update and Insert’, as seen in above screenshot for ‘ContactName’ column.

Push-Down Optimization

Once the layout is defined and flow executed, the Astera SCD transformation generates the SQL code necessary to compare, join, route, and insert data in your target dimension and pushes the transformation logic down to the database for processing.

Using this approach, the maintenance of large dimensions is significantly faster because all the processing is done by the database rather than the Centerprise server performing the operations and going back and forth between the database to read, compare, and write the data.

To learn more about the automated Slowly Changing Dimensions component in Centerprise and how to use it to manage your dimensions, download the white paper: How to Manage Slowly Changing Dimensions Using Centerprise.