When we develop security testing within inconsistent data volume situations, we should consider our use of anti-malware applications that use behavioral analysis. Many of these applications are designed to catch and flag unusual behavior. This may help prevent attacks, but it may also cause ETL flows to be disrupted, potentially disrupting our customers or clients. While we may have a consistent flow of data throughout a time period – allowing for a normal window of behavior to occur – we may also have an inconsistent data schedule or inconsistent amount of data that cause these applications to flag files, directories, or the process itself.
#etl #security #testing
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
The impulse to cut project costs is often strong, especially in the final delivery phase of data integration and data migration projects. At this late phase of the project, a common mistake is to delegate testing responsibilities to resources with limited business and data testing skills.
Data integrations are at the core of data warehousing, data migration, data synchronization, and data consolidation projects.
In the past, most data integration projects involved data stored in databases. Today, it’s essential for organizations to also integrate their database or structured data with data from documents, e-mails, log files, websites, social media, audio, and video files.
Using data warehousing as an example, Figure 1 illustrates the primary checkpoints (testing points) in an end-to-end data quality testing process. Shown are points at which data (as it’s extracted, transformed, aggregated, consolidated, etc.) should be verified – that is, extracting source data, transforming source data for loads into target databases, aggregating data for loads into data marts, and more.
Only after data owners and all other stakeholders confirm that data integration was successful can the whole process be considered complete and ready for production.
#big data #data integration #data governance #data validation #data accuracy #data warehouse testing #etl testing #data integrations
Data Loss Prevention is a set of tools and practices geared towards protecting your data from loss and leak. Even though the name has only the loss part, in actuality, it’s as much about the leak protection as it is about the loss protection. Basically, DLP, as a notion, encompasses all the security practices around protecting your company data.
Every company, even if never vocalized it, has or should have at least some DLP practices in place. You obviously use identity and access management that include authenticating users; you also for sure use some endpoint protection for users’ computers. Maybe (and hopefully) you do beyond that. And this all can be called data loss prevention.
#data-protection #cybersecurity #data-backup #data-security #data-breach #personal-data-security #data #cyber-security
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.
Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.
#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt