You have a data warehousing project to deliver. What are the most advanced software options?

Cloud's a buffet of flexibility, but grab the right fork! Here's my 2-cent recipe: Start Open Source: Tools like DBT & Dagster (free!), let you dip your toes in data transformation & orchestration without infrastructure headaches. Managed Marvels (Later): As needs grow, managed services call, but remember, convenience costs! Budget Buoys & Alert Anchors: Set credit checks & alerts to avoid bill tsunamis. Think latte money, not small country's GDP! Escape Velocity from Vendor Lockin: Choose open APIs & portability. Don't get chained to a single platform – keep your data free to roam! Cloud's your superpower, wield it wisely. Plan, monitor, avoid handcuffs. Happy data adventures! ?

Leading DataWarehouse solutions like Snowflake, Amazon Redshift, and Google BigQuery share fundamental capabilities for storing and querying large datasets for analysis (OLAP). A notable trend is the transformation of these warehouses into powerful data platforms, integrating advanced features like real-time streaming, embedded datalakes, seamless REST API integration, multi-language querying, and even OLTP capabilities. Data modeling remains crucial for optimizing query performance, ensuring governance, and providing a structured foundation for analysis and reporting

If you have a requirement of extract SAP data, you must need avoid indirect access and also use the best practice to consume this data using SAP Landscape Transformation Replication Server (SLT) or CDS Views based replications. In both cases the best Cloud solution to achieve this requirement is SAP Datasphere, that have a capacity to extract and store this data in an Data Warehouse layer or also extract and send this data as files to any other cloud based Data Store like AWS S3, GCP Storage or Azure Object Store.

Data Warehouse is place where you can archive huge volume of data and process the data to get insight on Ad hoc bases. Cloud base solution serve best because of cheap storage, further there are archiving policy where you can reduce your storage cost more and you can spin up cluster on need to process that data, you don't need to maintain heavy infrastructure. Since storage is separate from processing you can always disable your cluster when not in use saving more cost. For Storage: - ADLS (Azure Data Lake Storage) For Processing data: - Databricks For visualization: - Power BI

First and foremost, it's crucial to assess the client's level of maturity: what existing technical and functional capabilities they possess in-house and their future aspirations. A Data Warehousing project is not a one-size-fits-all solution. If the client has a cloud-oriented perspective, undoubtedly, we should consider solutions like Azure, GCP, AWS, etc. Conversely, if they favor an on-premises approach, a solution such as Cloudera, with its hybrid capability, might be more appropriate. Additionally, we often encounter clients adopting a hybrid approach, where raw data remains on-premises, some ETL processes are executed in a "fog" zone, and then data is transferred to the cloud.

Data lake enables collection of almost any kind of data. However, that does not mean that a data lake is a data dumping ground. The data inside the data lake must be organized in such a way that it can be easily found and used subsequently. In recent years, many vendors have taken an approach of becoming a one-stop-shop for all of the services, offering their implementation of a data lake as part of that. Examples include Snowflake and Databricks. As a result, now there are more choices than ever for either using a data lake independently, or as part of vendor-offered ecosystem.

Data lake integration involves combining structured and unstructured data from diverse sources. A data lake acts as a repository, storing raw, unprocessed data, while data warehousing organizes and processes this information for analysis. Integration ensures a seamless flow of data between the data lake and the data warehouse, allowing for comprehensive analysis.

The evolution of data management over the past two decades has been fascinating. When I started my career in data warehousing 20 years ago, Operational Data Stores (ODSs) were a popular solution for staging and preparing data for analysis. While this approach served its purpose, it often required careful planning and schema definition, potentially limiting its flexibility. Today, data lakes have emerged as a powerful concept, offering greater scalability and the ability to store all types of data, structured and unstructured alike. However, it's crucial to remember that a data lake is not just a "dumping ground." Effective utilization requires clear objectives and a well-defined governance strategy to ensure data quality and accessibility.

Integration with data lakes is a valuable feature in modern data warehousing software. Data lakes store vast amounts of raw and unstructured data, crucial for analytics and machine learning. Integrating your data warehouse with a data lake, as offered by platforms like Databricks, AWS Glue, and Delta Lake, allows you to query and analyze data from both sources. This dual-access approach enhances flexibility and deepens insights, combining the structured environment of data warehouses with the expansive, raw data of lakes. It represents a holistic approach to data management, catering to diverse analytical needs and maximizing the potential of big data.

Data lake integration is a critical aspect of modern data management strategies. it serves as a centralized repository for storing raw, structured, and unstructured data. Integrating a data lake involves seamlessly connecting it with various data sources, processing engines, and analytical tools. This integration helps the organizations to harness the full potential of their data by facilitating efficient data access, processing, and analysis.

Tableau, PowerBI, Looker are BI tools, not "data warehousing software platform". A modern data platform should handle the whole flow including data ingestion, optional data transformation, and then a strong semantic layer that can serve the business user's requests. Rich functionality may introduce difficulty for some end users to perform self-service analytics. They are not looking for fancy visualizations, but reliable and timely data delivery and be able to quickly analyze data with a high performance interactive experience. Many "Self-Service" may be replaced with the Copilot like experience. The users will ask their business questions, like interacting with chatgpt and the insights will be generated.

In my experience Data warehouse using enterprise cloud platforms are better suited to keep the infrastructure cost low. The key issue till date is quality of Data especially when you have several legal entities operating with legacy system. So addressing Data quality is a significant step towards a stable data ware house system.

- Empowering End Users: Self-service analytics democratizes data, giving power to the hands of end-users. This shift fundamentally changes how decisions are made, moving from a top-down to a more collaborative, bottom-up approach. - Rapid Decision Making: By cutting out the middleman (data experts), businesses can react in real-time to emerging trends and insights, significantly speeding up the decision-making process. - Innovation Through Exploration: When users can explore data on their own terms, it paves the way for unexpected insights and innovations. However, democratization comes with its own set of challenges, ensuring data quality, governance, and literacy among all users is crucial.

In my experience and even thought process earlier it was evident that future is more about self service analytics. IT people will expose data through various data models which are exposed through tools like Qlikview, Tableau, BOBJ , Power BI and so on..End uses, superuser are building the self service analytics and their own custom reports for their day to day use or reporting to higher management. The turnaround time for the business through self service analytics is improved..Through Qlik sense , vibe portals which are enables end users to use specific data sets for their end user reporting..Customers are also using tools like alteryx to build the self service analytics in quick time..

I am an Evangelist for self-serve analytics: Put more ownership into the different teams working with data. Remove dependency on individuals which can slow down the decision making process. The platforms for self service analytics also helps reduce the time it takes to prepare reports manually. The self-serve platforms enable teams to play around with numerous forms of visualizing data in order to Better depict the story behind the numbers.

In addition to the tools mentioned above, major cloud providers have data catalog, lineage and other governance features integrated with the data solutions. Examples are Unity catalog from Databricks and Azure Purview. Depending on the scale of data platform and business impact, an assessment should be conducted for current and future data governance capabilities needed by business and cost/value lens applied to select the right tools; many of them come with high licensing costs.

Having tools alone doesn't ensure data quality and governance. Establishing robust processes and methodologies is crucial. Implementing data contracts and similar measures proves beneficial. These frameworks provide a structured approach to defining, managing, and ensuring data quality and governance. By formalizing expectations and standards, organizations can better control and maintain the integrity of their data, contributing to more reliable and effective data management.

Data governance is essential! Data catalog, lineage, data quality, data security are all art of the game. Have policies, procedures and rules from the beginning of the project, is the key to good governance.

Tools are a helper but having a concise governance framework with people , process and tech focus is what you need to drive the adoption.

Getting data quality right in data warehousing projects is super crucial because it's the backbone of reliable insights and smart decisions. The big challenge? Dealing with messy data—think inconsistencies, missing info, and data errors creeping in during integration. It takes a lot of time and effort to clean up and keep the data in top shape. Plus, as more data sources pile up, it gets even trickier. To stay on top of it, organizations need solid data governance and quality management strategies to keep the data game strong throughout its lifecycle.

I'm going to throw predictive analytics and data science in this bucket also. AI is a broad term and covers a lot of functionality. However, the value of this group is the ability to discover patterns, relationships, and insights that humans may never have considered and improving our understanding of what truly drives outcomes. Interestingly, the technologies have been around for a while. It's the integration of the tools into single cloud-based platforms for each vendor and the ease of use and ability to rapidly develop and implement solutions that has allowed many organizations to finally move forward with these technologies as a core part of their solutions and strategy.

Ich bin so froh das das ganze ML und AI aus der Nische drau?en ist. Schaut Euch zum Beispiel die Vector Engine in der SAP HANA Cloud ab Ende M?rz an. Das ist der neue hei?e Scheiss - und man braucht dafür keine externe Engines. Datenschutz wird ebenso gro? geschrieben.

In order to enable AI/ML in a data platform, it is advisable to have some additional mechanisms in place. One of the possible ways to do it, is to give access to a curated dataset, specifically for that purpose. It may reside in the data lake, or elsewhere, depending on the use-case. This will give the ML engineers / data scientists access to the data in an organized and secure manner.

Starting the journey with AI/ML it is important to understand the use case and the data as well as the underlying platform. Will your system scale, with storage and compute, and does it provide the capabilities you will need to meet your needs? Having used several large scale DW systems it's critical to understand their limitations. Having the data already present is a huge time saver as well. Another critical factor is adoption and integration of the latest in LLMs. Finally security... is your data truly secure in the platform and integrations that are built.

For advanced data warehouse projects, Snowflake, Amazon Redshift, and Google BigQuery are popular cloud-native options known for their scalability and performance. Microsoft Azure Synapse Analytics offers seamless integration with other Azure services, while Apache Hadoop and Apache Spark provide distributed processing capabilities. Teradata is another established option known for scalability and advanced analytics features. Each option has its own strengths and should be evaluated based on specific project requirements.

Data strategy and data architecture are most important to the overall success and long term cost of any data warehouse project. Data Strategy - aligns the business objectives, outcomes and priorities - with the measures & KPIs - data - technology - and stakeholders & consumers Data Architecture (Data Topology) ia developed (logical, physical) from the data strategy - starts with the edge (data consumers & producers) instead of starting in the center like traditional warehouse approaches - identifies users (personas, APIs, devices) and uses of data - defines data zones (what goes where and why) - puts this in motion with data flows and processing (movement, transformation, embedded logic and AI models, signals and data quality, etc)

More important than the implementation technology is getting the design right from the start. Best way to do that is to use something like ERwin, Toad or MySQL workbench to reverse-engineer existing systems of different types and combine them into a cohesive physical data model. From that (working with the users) you can derive the logical model you want for your new warehouse, usually denormalized. Then use the tool to generate the DDL for your new environment wherever that may be. Then create ETL processes to pull/update the data on a regular basis. Last step is defining the facts, metrics and dimensions you want to provide for your reporting capabilities and working with the users to give them the reports and dashboards they desire.

With increase in analysis of interrelated data , Graph databases should be considered for data warehouse projects due to its effectiveness in analyzing deeply connected data . They are very useful in use cases in recommendation systems , fraud detection , social network analysis etc .Major advantages of these are that they store relationships and can avoid using expensive joins increasing the performance. Also these are more flexible to changes in data models.

An dem Weg in die Cloud wird mittelfristig kein Weg vorbeiführen. Man kann daher jedem Unternehmen im Financial Services Umfeld nur raten, sich m?glichst zeitnah mit der Frage des ?Wie“ auseinander zu setzen. Der Einsatz von Cloud-L?sungen und die Integration in bestehende Architekturen und Prozesse stellt ein umfassendes Change-Vorhaben dar. Hier bietet sich ein schrittweiser Einsatz und der parallel der stufenweise Aufbau des notwendigen Know-Hows an. Wichtig ist, dass die Unternehmen ausgehend von der ?Cloud-Readiness“ der heutigen Aufstellung und den ?Schmerzpunkten“/ Anforderungen einen priorisierten Migrationsplan entwickeln. Hierdurch k?nnen schnelle Erfolge erzielt und eine strukturierte Transformation hin zur Cloud erfolgen.

Each solution has advantages and disadvantages, mainly depends on cost effectiveness and security concerns (layers, metadata, backups, encrypted, connectivity) and faster implementation.