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Apache Spark to Redefine Data Analytics for Advertising Industry
CIO Review
An Overview
Developed in Berkeley's AMPLab of the University of California, ‘Apache Spark’ has set the bar of analytics on fire. Technology experts are admiring ‘Spark’ for its greater compatibility with various technical platforms such as Java, Python, SQL, and others. CIOs and CTOs find the tool very handy to formulate business decisions. They are actually keeping the information at their fingertips and controlling the contents as per the needs. Apache Spark is not only limited to IT environments but the technology is expanding its wings in the advertising industries as well.
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Apache Spark and Advertising Optimization
Often, advertising companies deals with the pile of data in the form of video, text, or audio and sometimes with all three. Using the right data at right time and at right place is the principle advertising companies follow to educate targeted audience through info-graphical means. They therefore need to have accurate information of data using appropriate tools. In the same way, BlackArrow Inc. was in search of genteel analytical solution to grab larger audience with its video advertising expertise.
Joe Matarese, CTO of BlackArrow Inc., was astonished by scalability of ‘Spark’ and viewed it as a must solution for its firm which deals with big data. BlackArrow assist cable TV operators by broadcasting ads meticulously for targeted audiences across TV services or online platforms. The firm implemented ‘Spark’ under the leadership of Matarese which enabled BlackArrow to collect data from cable operators seamlessly. The data collected contained information regarding basic information about viewers and shows watched by them, and also about platforms used to broadcast TV shows. Using this information, the firm determined what types of commercials are needed to be played at the appropriate time based on the characteristics of viewers. Spark then moved the collected data forward and with self-scrutiny features it furnished information about which ads viewers viewed or skipped through Infobright analytical database.
The data is stored in the form of reports which can be accessed by cable operator customers to view or carry out polarized analysis on effectuality of ads. It is evident now how easy it is to use the Spark with visualization tools and Pentaho’s business intelligence solution embedded within it. “We chose Pentaho over other data visualization vendors because it offers greater control over how reports are surfaced to BlackArrow's clients. Spark also has good adoption and a development community, which is why we love it, but it's still a relatively young technology. The attraction is that there are a lot of capabilities, and we expect to make use of those capabilities,” says Joe Matarese, CTO, BlackArrow.
Spark functions with effective data processing speed with SQL queries to fetch and liberate information. “The company previously used a homegrown reporting system, but customers started wanting to do more ad hoc querying, which prompted BlackArrow to decide it needed a packaged tool that would support static reports as well as user queries,” said Matarese.
How Safe Is ‘Spark’?
Databricks, creator of ‘Spark’, has already elucidated that it is free of warts as it bring out stability in processes to large extent with seamless data processing speed. Its ability to incorporate latest IT technology feature is like icing on the cake as it fulfills every aspects of data analytics. But you may encounter inconsistency between organization processes and the new and youngest of ‘Spark’ version due to the developmental differences. Matarese further opined that, “Solving those kinds of issues takes persistence and an understanding that a Spark implementation is going to require ongoing development and maintenance. You're going to have to deal with a few hurdles.” But if your system runs on any of the technology supported by Spark, it will be easy for you to get analytics easily done.
Reaching out to customers with valid data is the first priority of every organization and ‘Spark’ surpasses the expectations with its potential analytical features. Spark can be viewed as a sequel of Hadoop data analytics solution while others view it as a replacement for Hadoop. “It all goes back to understanding what you're trying to do and the problems you're trying to solve,” Matarese advises.
How Apache Spark Is the Next Big Data Analysis Thing!
Big data is not just about data but it is more about what you do with the data. With apposite analytical tools and statistical principles, vital information can be extracted from data that can help CIOs to boost up business. In the same way, the ‘Apache Spark’ is viewed as next big data analytical tool for advertising companies.
CIOs have access to various analytical tools available in the market but MapReduce had always been the chosen one. It allowed them to manipulate large chunk of data on distributed computing platform. But despite being one-of-its-kind, MapReduce was hit back due to slower speed. MapReduce users could have experienced discomfort and they were in search of robust and feasible solutions that could help them to overcome the issue. By understanding the need, Databricks assembled the ‘Spark’ as a next generation data processing engine. But, since the inception of the ‘Spark’, it has always been compared with MapReduce as expectations with the new tool were sky high. “MapReduce is an implementation of a design that was created more than 15 years ago. Spark is a from-scratch, reimagined or re-architecting of what you want out of an execution engine given today's hardware,” said Patrick Wendell, Co-founder, Databricks.
Unlike MapReduce, the Spark does not work in batches instead it processes chunk of data together to streamline data analytics process. The Spark also eliminates limitations of data in-memory process faced in MapReduce. By overcoming all the hurdles faced by using MapReduce, the ‘Spark’ was started to seem as a ray of hope in data analytics platform by CIOs. It doesn’t end there as Databricks further stated that the data analytics tool will also be compatible with Hadoop and could redefine big data ecosystem. Large organizations such as Novartis, Comcast, and Goldman Sachs are already praising about what miraculous changes Spark can bring out in data analytics. “I do think Spark has a role to play and a life that's outside of the Hadoop environment. I hope Spark transcends the label, and I think to a large extent, we've done that,” Wendell added.
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Distributors can have plenty of inventory across their network and still struggle to meet demand at individual branches. Stock sitting in one warehouse does little good when customers need it elsewhere, while keeping every location stocked for its highest historical demand can tie up unnecessary capital. With thousands of product and location combinations to manage, purchasing teams cannot review every decision manually. Executives evaluating demand forecasting software need to know whether it can improve local product availability without driving up inventory costs across the business. Network-wide averages can easily hide costly gaps at individual locations.
Past sales provide a useful starting point, but irregular buying patterns can make forecasting difficult. Slow-moving products may not generate enough transactions to produce a reliable forecast on their own. Seasonal spikes can also skew averages long after demand has returned to normal. Buyers should look at whether the software can draw insights from related products and distinguish recurring demand from unusual orders. External factors such as weather may also help improve predictions, but their value needs to be demonstrated for the products concerned rather than assumed across the entire catalog.
Knowing what customers will buy is only half the equation. Buyers also need to know when to reorder. Changing supplier lead times can make it difficult to act on even an accurate demand forecast, while order frequency and minimum purchase quantities affect how much stock needs to be kept on hand. Software should account for actual purchase order history rather than rely solely on fixed lead times. Safety stock should also reflect how individual products sell at each location. Applying the same buffer everywhere can leave fast-selling products out of stock while slower-moving items pile up.
“Lantern refreshes recommendations weekly and lets buyers compare suggested stocking levels with existing settings before approving or overriding them.”
The way forecasting software works with existing ERP systems is another important consideration. If recommendations sit in a separate dashboard, buyers may still have to enter them manually into their purchasing systems. They should establish whether the software can read transactions from their existing ERP and feed approved stocking parameters or purchase orders back into it. Buyers also need to understand why the system recommends a particular change. When a business wins a new account or a branch receives an unusually large order, experienced staff should be able to override a forecast without having to work outside the system.
Forecast accuracy matters, but it does not tell the whole story. Lower inventory means little if it leads to stockouts and unhappy customers. In some cases, holding more stock may be the right decision if those products sell consistently. Buyers should look at how the software affects product availability, inventory turns and the capital tied up in excess stock. Testing its recommendations against past transactions can also show where they differ from existing purchasing practices before a wider rollout. Buyers should also account for the work involved in preparing historical data and managing exceptions once the system is in place.
Lantern builds a custom machine learning forecasting model for each distributor, using historical ERP data and business-specific purchasing practices. It combines demand forecasts with projected supplier lead times to recommend order timing and safety stock at the item-location level. Lantern refreshes recommendations weekly and lets buyers compare suggested stocking levels with existing settings before approving or overriding them. Approved purchase orders or stocking parameters can be written back into the ERP. Its models also examine relationships across products, supporting forecasts where individual sales histories are sparse. Distributors with large catalogs spread across multiple branches, especially those balancing excess inventory against missed sales, are the relevant buyers for this model. ...Read more
Enterprise imaging purchases rarely fail because a scanner cannot create a digital file. Trouble starts when document volumes and legacy applications collide with downstream workflows that were built around older processes. A platform may handle clean, standardized forms well, yet slow down when incoming files vary substantially or demand different routing rules. Executives, therefore, need to examine how completely a proposed environment can absorb the real document mix without forcing extensive manual preparation or creating new bottlenecks elsewhere.
Capture architecture deserves close attention as the front end determines how much work remains after scanning. Useful systems should support classification, optical character recognition, data extraction and validation in ways that match actual document types and exception rates. Human review also matters. Automated recognition is valuable when confidence is high, but buyers should understand where validation screens enter the process and how questionable fields are routed before information reaches a database or content repository.
Hardware choices belong in the same discussion. Desktop scanning and high-volume production work impose very different demands, and forcing both through a single equipment model can inflate costs or limit throughput. The proposed design should reflect actual preparation effort, peak intake volumes and the physical scanning environment rather than nominal device speed alone. Scanner flexibility also matters where several departments process different document sets at substantially different volumes.
“EDAC Systems provides first-line support across the hardware and software it sells, reducing the need for customers to separate faults among multiple providers.”
Legacy compatibility is equally consequential. Many enterprises still depend on older capture software and long-standing repositories while retaining scanners embedded in established processes. Replacing everything at once can increase implementation risk and budget pressure. A stronger proposal should account for migration and configuration from the outset, with integration planned around how existing records move into the new environment and how the changeover will be tested.
Buyers should also examine whether the architecture can adjust when document volumes increase or automation requirements change without requiring a wholesale rebuild. That flexibility becomes more important when an organization expects its capture requirements to shift over several budget cycles.
Security and records requirements can narrow the field quickly, particularly in government or regulated environments. Encryption and records-management controls need to be resolved against approved deployment conditions before installation, rather than treated as post-deployment configuration. Procurement teams should verify that both hardware and software satisfy applicable requirements and that the provider can work within on-premises or approved hosted environments when needed.
Support after go-live is another practical separator. Document imaging environments combine scanners, capture software, connectors and repositories, which can leave customers coordinating multiple suppliers when something fails. Contract review should establish who owns first-line troubleshooting and whether hardware and software issues are handled through one support path. Availability of on-site assistance also matters when remote fixes are insufficient.
EDAC Systems designs document imaging and data capture environments around each customer’s document types, workflow, existing technology and budget constraints. Its approach can combine Ricoh or OPEX scanning hardware with capture software and Doxis-based document or records management, depending on the application. It also handles migration, configuration, testing and training during implementation.
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Moving from Google Workspace or legacy Exchange to Microsoft 365 involves more than transferring mailboxes. File structures may not translate cleanly, account permissions can change and an overlooked dependency can interrupt access after cutover. Executives comparing migration providers must establish how thoroughly each one investigates the existing environment before committing to a transfer schedule. A technically completed migration offers little reassurance when employees cannot retrieve the information they previously used.
Pre-migration assessment establishes the boundaries of the work. User inventories should account for inactive accounts and shared mailboxes, while file assessments must identify naming restrictions, excessive path lengths and permission structures that Microsoft 365 handles differently. Tenant-to-tenant transfers introduce additional complications around identity mapping and existing collaboration spaces. Providers should document these differences before migration begins rather than discovering them during execution.
Compliance requirements can alter the migration design considerably. Retention policies and access restrictions may determine where information resides after transfer or which users can retrieve it. An assessment that concentrates exclusively on data volume risks overlooking controls that governed the original environment. Procurement teams benefit from examining whether the provider produces a documented migration scope connecting technical dependencies to the organization's internal requirements.
“RyanTech incorporates Microsoft Defender and Purview into tenant planning, allowing security requirements to shape the destination environment before users begin working in it.”
Cutover scheduling exposes a different set of risks. DNS propagation may take longer than anticipated, while network interruptions can delay synchronization during a planned transition. Promises of zero downtime leave little room for these external dependencies. A credible migration approach includes contingency procedures, defined escalation responsibilities and a cutover window that reflects the organization's working patterns. The provider should explain what happens when an expected transition does not occur on schedule.
Business continuity also depends on what employees encounter after the switch. Missing shared files or unfamiliar permissions can generate support requests even when mailbox transfers succeed. Validation should establish that users can access the resources required for their work, not merely confirm that data reached its destination. Clear responsibility for resolving post-cutover issues reduces the burden on internal IT teams.
Security configuration cannot be deferred until employees have settled into Microsoft 365. A newly provisioned tenant may introduce unfamiliar exposure through identity settings or insufficiently configured data protection policies. Microsoft Defender, Intune and Purview provide controls that can be incorporated into the destination design. Providers differ in whether they configure these protections during migration planning or treat them as later projects. Licensing decisions also influence which controls are available and whether organizations purchase functionality they will actually use.
Support arrangements become particularly relevant once the migration specialists have completed their assignment. Changes to compliance requirements or user access may expose decisions made during the original transfer. Continuity between migration and support personnel helps retain that context.
RyanTech conducts Microsoft 365 migrations through an initial environment audit that establishes customer requirements and informs a documented transfer plan. Its migration team prepares for DNS delays and other cutover interruptions, scheduling transitions around business requirements rather than promising uninterrupted service. RyanTech incorporates Microsoft Defender and Purview into tenant planning, allowing security requirements to shape the destination environment before users begin working in it. Its project and support teams subsequently address configuration changes, user adoption and evolving compliance requirements. Experience spanning Google Workspace, on-premises Exchange and tenant-to-tenant migrations informs its handling of differing source environments. Organizations consolidating disparate systems into Microsoft 365 while requiring security configuration and continuing technical support are particularly suited to this approach. ...Read more
The management of cloud environments involves the proper coordination of infrastructure, access rights and resource allocation as companies expand their operations digitally. In Latin America, organizations adopting cloud-based systems must account for regional data protection requirements, varying infrastructure capabilities and the complexity of overseeing multiple service providers.
Cloud management is an effective tool that enables the IT professionals to control the workloads, ensure the standard configuration, resource consumption and detect the possible disruption of services that could impact critical business operations. Clear governance policies, automated monitoring and well-defined access controls can help businesses maintain reliable cloud environments while managing operational complexity.
How Does Cloud Management Benefit Businesses?
Cloud management makes it possible for companies to have more options when dealing with varying loads without making significant investments in infrastructure. Organizations can scale computing capacity to match project demands, support remote teams and deploy digital services across different locations. For companies operating in Latin America, this flexibility can help accommodate expansion into new markets while allowing technology resources to evolve alongside business requirements.
The other significant benefit relates to visibility in finances. Centralized oversight helps organizations understand spending across cloud services, identify underused resources and select suitable service plans. These insights support more informed budgeting and reduce the likelihood of paying for capacity that delivers little practical value. Businesses can also evaluate service usage against departmental needs to allocate technology budgets more effectively.
Cloud management can be an important tool to improve the continuity and collaboration of business operations. Backup planning, recovery procedures and coordinated service administration help organizations prepare for unexpected outages and restore essential applications when disruptions occur. Shared digital workspaces and accessible business applications can further support collaboration among distributed teams. For businesses in Latin America, these capabilities can help maintain consistent operations across regional offices while supporting day-to-day productivity.
What Are the Emerging Trends in Cloud Management?
Artificial intelligence is reshaping cloud management through intelligent analytics, predictive monitoring and automated decision-making. These tools can help IT teams identify unusual performance patterns, anticipate capacity demands and investigate potential technical issues before they escalate. In Latin America, growing interest in AI-enabled infrastructure management is creating opportunities for businesses to simplify complex technology environments while improving the speed and precision of routine IT operations.
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