You're starting out in Data Warehousing. What challenges should you prepare to tackle?

Data ingestion:Data from one system is integrated on a timed basis into another system Another type of data integration consists of a specific set of processes called Extract,Transform, Load( ETL) Data integration may also include: Cleansing Sorting Enrichment Apart from ETL, ELT some other strategic types of data integration are: Data replication Data Virtualization Change data capture Streaming data integration Data integration helps to deliver valuable data in terms of integrity and quality Data integration provides a bird's eye view of business analytics, insights and intelligence Data integration platforms involve tools for: Master data management Data migration Data connectors ETL Data governance Data catalogs

A better design of the dimensions and facts makes ETL process more efficient and faster. Also helps minimize data redundancy and optimal usage of the memory.

So first of All 1. Understanding Business Needs Ensuring data warehouse design aligns with business OKRs and stakeholder expectations which can be challenging, especially if requirements are unclear. 2. Data Integration and Consistency Integrating data from multiple sources with varying formats and data quality can lead to inconsistencies which will result to unreliable insights, affecting the value of the data warehouse. 3. Data Modeling and Schema Design Designing a schema that balances performance with flexibility for future needs can be challenging. 4. Costs Management Data warehousing can be costly, especially with cloud storage and processing fees.

Key data integration challenges: 1. Data Silos: Integrating isolated data across departments. 2. Data Quality: Ensuring accuracy, completeness, and consistency. 3. Data Transformation: Converting formats to a common structure. 4. Real-time Processing: Handling high-velocity data for timely insights. 5. Scalability: Supporting growing data volume and complexity. 6. Latency: Reducing delays in data availability. 7. Security and Compliance: Protecting data and meeting regulatory standards. 8. Heterogeneous Sources: Integrating various data systems and formats. 9. Error Handling: Managing issues during data transfer. 10. Cost: Balancing integration expenses with project ROI.

Para se preparar para implementar um data warehouse, siga estas etapas essenciais: 1. Definir Requisitos de Negócio: Alinhe o projeto com as metas da empresa e engaje os stakeholders para entender as necessidades. 2. Planejar a Arquitetura: Escolha um modelo de dados (como Star Schema) e defina a estrutura das tabelas para suportar relatórios e análises. 3. Garantir a Qualidade dos Dados: Concentre-se na limpeza e padronização para evitar problemas de inconsistência que podem comprometer as análises.

As per my understanding first we have to check the data volume and what kind of transformation is required and at which interval it is required.

Before we start to ingest data and start ETL or ELT processes from different source systems, it's important to understand the nature of incoming data interms of frequency, size,format and how integrable the source system is. So it will help us decide which ETL tool or programming language we use to ingest the data and we can handle the next steps of data warehousing easily.

Platform Choosing the right provider carefully is the first and most critical part of your journey. Evaluate their service offerings (IaaS/SaaS/PaaS), cost and pricing structure, scalability, and performance factors based on your needs. Endpoints While designing warehouse it is also equally important to look at decommissioning your legacy systems and databases which might become performance and scalability bottlenecks as you start dwelling into AI models that need massive amounts of data and faster data retrieval. Beyond ETL Look at all the other aspects like external scripts, schedulers, and custom code in the ETL flows that control the overall orchestration of the code and plan to modernize them as well.

The Data Quality Assessment Framework (DQAF) is a set of data quality dimensions, organised into: Completeness Timeliness Validity Integrity Uniqueness Consistency Most data managers assign a score of 0-100 for each dimension Completeness: measure of the percentage of data missing within a data set Timeliness: measures how up-to-date or antiquated the data is at any given moment Validity: information that fails to follow specific company formats,rules, processes Integrity: the level at which the information is reliable and trustworthy Uniqueness: compiling unique customer information while including each customer's associated performance analytics Consistency: source of information collection should capture the correct data

Data Quality Framework when implemented right will save $ and person hrs for the organization. While implemeting DQ framework, another key component to watch for is the DataDecay

The key question is whether you have dedicated data quality tools at your disposal. If so, you can use them to profile and assess data quality. As a consequence of profiling, you will be able to create rules that will help improve data quality on the one hand, and on the other hand provide an assessment of that quality over time. If you don't have data quality tools at your disposal the implementation of improving and measuring data quality will have to be done by you directly in ETL tools or in SQL/Python code. This is a much more difficult path.

Data quality is critical since inconsistent or inaccurate data can compromise the integrity of the warehouse. Addressing missing values, duplicates, and conflicting data sources is essential to ensure reliable insights and seamless flow into downstream applications.

High data quality is crucial, as incomplete, inaccurate, or irrelevant data can undermine insights. Addressing this requires implementing strict validation rules to catch errors early, establishing automated data cleansing routines to remove duplicates and correct inconsistencies, and regularly monitoring for anomalies. Maintaining data quality is an ongoing task that involves attention to detail and a proactive approach to identifying and addressing issues. This continuous process helps prevent data degradation and ensures reliable analytics, supporting better decision-making and maximizing the value of the data warehouse.

Data Quality has to be on key attributes of source and Target layers which are integral part of your transformation and business KPI's. Data Quality rules must be rechecked for existing rules that can be upgraded to meet new business/regulatory needs and/or adding totally new DQ rules based on new source attributes (in case of M&A) and sometimes business request extra checks. Meeting with Stakeholders are important on a monthly basis to understand the beat of DQ checks. DQ Dashboards infused with AI for monitoring threshold of DQ checks and taking realective task actions are typical trend these days.

Do data profiling. Then raise any data quality issues with the business sponsors. Possibly a clean up initiative and quality monitoring process can be established

From what I lived so far, I don't like that much to split the Data Quality from the Data Integration. When you develop an ETL process, think as much as possible about all the checks/safegaurds you can put in place regarding data quality. This will really help you to wisely choose the use cases when you need to reject the full update, just reject a record and log it, or insert the record but log it in an error file. Mastering when to make fail what is paramount to keep data quality to its best. It is sometimes better to not load data than loading incorrect data, as long as your users are aware.

Yes agreed totally But if you have good data quality tool, good data transformation tool So it is easy to check and ensure the quality if data It will require the developer attention as at any point it will fail you have to fix But now there are good features where you can add you mails or slack into alert part of it.

Below is high level approach: 1st assess the existing tool which your working with, and identify if this provides you result which is intended or required by stakeholder. 2nd identify your CDE's in relation to your specific domain and discuss with DAO or DSAO on establishing rule in relation to DQ Dimension. 3rd establish data contract and ownership of CDE's to track DQ issue. 4th use of data observability tool is very important to understand changes in schema or quality threshold of CDE's in respect to DQ and it matter to keep data producer and consumer updated about any change happens proactively.

The internal tables in the data warehouse are divided into: Staging Layer Core Layer Semantic Layer Staging Layer consists of a set of tables. They are structured to match the structures of the source systems from which the data is coming Core Layer is a place of storage for combined and normalized data. The data which comes into this layer should be a clean version of the data Semantic Layer is the final layer of filtered and formatted data. Two primary scaling approaches may also be used for Scalable Data Architecture: Horizontal scaling Vertical scaling Horizontal scaling is like building a team of superheroes Vertical scaling is giving a mega boost of power to a single hero Partitioning data ,sharding achieves scalability

Architecture should also consider data security, user authentication mechanisms and handshake when multi cloud platforms exist. This will be critical for long lived reporting solutions. This kind of foresight, consulting and planning will reduce frequent changes to tools/technologies thereby better business user experience and self service.

Most important to make sure every Business has the data dictionary ready which causes all those struggling towards data discrepancies. How we interpret data while we are mapping with the Business . If we understand clearly what this data dictionary and what it’s mean l, our job will be much struggle free to perform end to end deliverables.

Do not hesitate to go and seek for help on that point, speaking with people already using the stack you are thinking off. Scalability is key, there is nothing more frustrating for your devl and your users that to keep doing technical projects because you encounter the limits of a new tool every X months. These limits are most of the time not so much tools/software limitations, but are linked to your modeling and your strategy. Think long term.

In SAP BW, building a scalable data warehouse architecture requires careful planning to handle increasing data loads and complex analytics. Leveraging SAP HANA as the database provides robust performance and scalability for large datasets, enabling faster processing of complex queries. Key architectural choices include optimizing data models to reduce redundancy and implementing partitioning and indexing to improve load performance. Additionally, integrating SAP BW with cloud solutions like SAP Data Warehouse Cloud allows for elastic scalability, accommodating both on-premise and cloud-based data sources. This hybrid approach ensures flexibility for future growth while maintaining efficient operations.

Costos de almacenamiento: A medida que el almacenamiento crece, también lo hacen los costos, tanto en infraestructura en la nube como en instalaciones propias. El control de costos implica optimizar el uso del almacenamiento y explorar soluciones de almacenamiento en la nube que se ajusten al presupuesto.

As data grows, your architecture must be capable of handling larger volumes without performance degradation. It’s essential to design a flexible infrastructure that can scale and adapt to increased demand.

As data volume will grow over a period of time, queries returning large volumes of data need to be handled efficiently. Different techniques(indexing,partitioning, archiving etc) for handling performance to make the architecture scalable should be considered during design.

- Creating a good data model that is easily scalable in nature is key for data warehousing. - Based on the use case, the models can either follow star or snowflake schema that can be easily translated to any BI tool. - Design and implementation of the model on any database should also focus on code efficiency and performance optimisation as a DW is typically huge because of historical data.

Key data modeling challenges in data warehousing: 1. Data Integration: Merging diverse formats and structures. 2. Consistency: Standardizing definitions, units, and formats. 3. Scalability: Designing for growing data volume. 4. Schema Complexity: Balancing normalization vs. denormalization. 5. Data Quality: Handling inaccuracies and duplicates. 6. Change Management: Adapting to evolving requirements. 7. Performance: Structuring data for efficient querying. 8. Historical Data: Capturing changes over time. 9. Business Alignment: Translating needs into models. 10. Security: Protecting sensitive data and ensuring compliance.

When developing data models, especially in complex data warehousing and analytics projects, several key challenges emerge. Data Consistency and Quality: Ensuring data consistency across diverse sources is challenging. Inconsistent or poor-quality data can lead to inaccurate analytics, so maintaining high-quality data through validation and cleansing is crucial

One of the main problems with data warehouses is latency. This is especially true when these warehouses are used to support reporting off of OLTP systems. If your customers need or want real-time data but your ETL loaders are loading every 15 minute or worse, overnight, then you've missed the boat on satifying user requirements. The second major concern is to be fully cognizant of the business uses for the warehouse. There is a temptation to simply migrate everything, but your end consumers usually don't need all source data. So start small, and base your loaders on concrete reporting (or other) requirements. Third, it is valuable to do a POC, a proof of concept, to demonstrate the value and efficacy of the warehouse.

Here is the real key. The best devl on any language that does not know the various modeling techniques in Data Warehousing will make efficient code, but you will still encounter performance issue inserting or querying data, as well as on the data quality monitoring. BI Data Modeling is the core skill to develop, to know when it makes sense, now but also mid term/long term, to use a star, a snowflake or a big table is key to the scalability of the data warehouse you put in place. Also keep in mind slow moving dimensions, when you want to analyse data according to a current hierarchy or to what it was at a certain point in time in the past.

A good data model underpins effective data warehousing. Learning how to structure and optimize schemas (e.g., star schema, snowflake schema) to support efficient querying and data storage is foundational.

Depending on complexity logical or persisted 'views' can be added on top of the model for feeding the reporting layer. The data model may be tuned to a specific business consumer group which is also referred to as data marts.

When we are in an organization we are forced to use the tool which was selected by architect groups . We hot our heads to bring solutions and think out of the box to solve the issues when there are restrictions in the tool. So there is nothing like expertise in one tool. All we need is to try different ways to solve the problem in hand. My suggestion will always be to think simple and avoid complications.

The thing that I have come to agree is - Having the latest and greatest tools in your org doesnt always generate a great ROI, but having the right tools and being cost effective for your organization is far more important.

Keeping up with tech trends is essential to staying competitive and innovative in data warehousing. My perspective is that in an evolving landscape, we need to continually explore advancements such as cloud-based data warehouses, big data frameworks, and AI-driven analytics. Leveraging these trends could help us achieve more scalable and flexible infrastructures, optimize data processing, and derive more intelligent insights. By integrating these innovations into our data warehousing strategies, we position ourselves to offer high-performance, future-ready solutions that keep pace with industry demands and unlock new possibilities in data-driven decision-making

Data warehousing technology evolves rapidly. Stay updated on cloud solutions, real-time data warehousing, and ETL/ELT tool advancements, as well as how they align with your organization’s needs.

Keep the data warehouse strategy tightly connected to business goals, ensuring it supports decision-making effectively. Regularly gather feedback to adjust the data warehouse's approach as business needs evolve.

This is the first thing you need to be sure of! Knowing what users want to do with data will tell you a lot about data model, architecture and performance. Many projects start without clear overview of data analytics needs of end users which can end-up with lot of re-work, architecture or model changes. When to use star schemas and when they are not needed? All is hidden in well analyzed business requirements!

Challenge starts right from the first step, which is to deeply understand the business context of transactional data generated from source system, analyzing its granularity, needs for post processing to derive meaningful insights and if there are any potential inconsistencies arising from business process. Most of the DW experts are really good at tech side of it but without knowing the business domain, data cannot be modeled efficiently.

Aligning with business goals ensures data warehousing efforts drive value. Engage with business stakeholders to clarify needs, identify key performance indicators, and ensure your work supports strategic decisions.

De acuerdo a mi experiencia he llegado a la a una regla de oro en donde el almacenamiento de datos "Menos es mas". Siempre tendemos a guardar absolutamente todo "por las dudas" y eso lentifica y dificulta la busqueda de informacion.

To tackle business alignment challenges in data warehousing, start by thoroughly understanding each department’s key objectives and data needs, which helps shape a warehouse that delivers actionable insights. Engage stakeholders early on and maintain ongoing collaboration through regular check-ins, allowing for iterative feedback and adjustments as needs evolve. Implement a data governance framework to ensure data consistency, accuracy, and security, which builds trust and reliability in the data. Finally, focus on creating user-friendly dashboards and reports to make insights easily accessible, ensuring the data warehouse directly supports decision-making across the organization.