Developed by the National Institute of Standards and Technology (NIST),AES Encryption uses various key lengths to provide strong protection against unauthorised access AES performs operations on bytes of data The number of rounds depends on the key length: 128-bit key - 10 rounds 192-bit key - 12 rounds 256-bit key - 14 rounds AES considers each block as a 16-byte grid in a column-major arrangement Each round consists of 4 steps: SubBytes: implements the substitution. ShiftRows: each row is shifted a particular number of times. MixColumns: is a matrix multiplication. Add Round Key: the resultant output of the previous stage is XOR-ed with the corresponding round key 128 bits of encrypted data are given back as output

Managing ETL processes and data privacy in data warehousing can be troublesome. To manage ETL processes effectively, start by understanding the data and then process the data in various chunks instead of processing it in one go. While processing the data, analyze the process and use tools to automate the various steps involved. For data privacy always mask the sensitive data even if the data is moving inside the organization. For external transfers always use encryption and decryption (AES/SHA) to protect the data and also use dedicated point-to-point data transfer. After transferring the data always make sure that the connection is closed. #datawarehousing #dataprivacy #datasecurity #ETL

Balancing ETL processes with data privacy requires clear strategies: 1. Data Masking: Mask or tokenize sensitive data before loading. 2. Role-Based Access: Enforce strict access controls with RBAC. 3. Data Minimization: Extract only necessary data to reduce exposure. 4. Monitoring: Use tools like Splunk to track and detect anomalies. 5. Data Lineage: Track data flow with tools like Apache Atlas. 6. Dynamic Access: Apply fine-grained, policy-driven access controls.

To tackle ETL and data privacy challenges in your data warehousing project: 1. Optimize ETL Processes: Adopt ELT not ETL for efficiency, enable incremental loading, and automate validation checks to ensure data quality. 2. Strengthen Data Privacy: Implement role-based access control, data masking, and encryption. Comply with regulations like GDPR or HIPAA, and use secure protocols for data transfers. 3. Automate & Monitor: Automate workflows with orchestration tools, monitor pipeline health, and set up alerts for errors or breaches. 4. Engage Stakeholders: Collaborate with privacy teams, train your staff, and establish feedback loops to refine processes. 5. Retention policies: Create them 6. Do not use prod data in other environments

Some ways to get start : - with identifying pain point what breaking and some spent time - automate and optimize with what etl tools will used - improve data governance with data validation and use version control

Simples! First, do ELT not ETL, transform data in place after load. Helps with security too as your data isn’t transformed in another tool and stored in multiple places, but I’m getting ahead of myself there. Organise your transform processes where possible into common groupings (Delta merges, SCD2 etc) and run through a metadata driven framework. Altida data load accelerator is a great example, look it up. As far as security goes, you’ll never be able to better the encryption cloud platforms provide, so stop fighting it and move your data warehouse into a fully purpose built platform for analytics and AI (think Snowflake, Databricks etc.). Instead invest your time in dynamic data masking to ensure users only see what they’re meant to

The process must be incremental approach. Start with pain points of the data loads, quality of data, resources(memory, On prem vs cloud), man power and cost. Quality of data must be audited and start to use ETL tools that are offered by oracle, SAP BODS to improvement the dataflow and keep a central ETL system for data loads. Identify Manual efforts done by team and implement automated process like automating data loads, data validations that will save lot of man hours. Security at database level or table level with role based or user based security for better data privacy and start to adapt to latest 3rd party data masking tools for sensitive customer data. Lastly, always keep an open channel to integrate any new tools and systems.

Following are the things to get started - 1. Identify the pain areas & their impact on the Business/end user. 2.Talk to all the relevant stakeholders to eliminate competing priorities & then decide the top 3 priority pain areas that need resolution asap. 3. Assess the current ETL Architecture. 4. Change the ETL processes/tools if needed and address the pain areas immediately

Struggling to manage ETL processes and data privacy in your Data Warehousing project? Start by auditing your ETL workflows to identify bottlenecks and failures, then implement automated monitoring tools to ensure smoother data loading. For data privacy, enforce role-based access control and apply data masking techniques to protect sensitive information. Collaborating with stakeholders and aligning ETL processes with compliance standards can bridge gaps. A proactive approach ensures efficiency without compromising security. #DataWarehousing #ETL #DataPrivacy #DataManagement

One way to improve your processes is to use an ETL tool instead of using scripts. As an example Oracle Data Integrator is one such Handy ETL tool. Some of the advantages of using an tool like ETL are better version control, inbuilt error handling where in case of errors like Primary key violations data is inserted into error tables instead of query failing , dry runs to check syntax and see of query will execute in production without actually changing any data. Apart from this you should also implement data observability and data ops to check if things are running as expected without error. One such tool is data kitchen where you can for example write a test to check no rows are skipped between source and destination and send out alerts.