What emerging data warehouse technologies can you use to improve your data management?

In addition to scalability and maintainability, I believe that one of the most crucial aspects we tend to overlook is data governance and lineage in data warehouse. Data governance involves defining and enforcing policies and standards for data management, ensuring data quality, security, and compliance. Data lineage refers to the ability to track the origin and transformation of data as it moves through various processes and systems. It's crucial for ensuring data quality, compliance, and understanding the data flow within your organization. It's essential to conduct a thorough assessment and possibly conduct a POC to determine which cloud-based data warehouse aligns best with your data lineage and data governance requirements.

In the realm of Quick Service Restaurants (QSR), we embarked on a data-driven journey to revolutionize finance operations. Our mission was to centralize data from disparate sources, including SAP HANA, Point of Sales, FarEYE, CRM, CSVs, and HRMS, into a unified Data Warehouse (DWH). To tackle this challenge, we harnessed a powerful tech stack, including Azure Data Factory for ETL processes, Azure Databricks for data transformation and advanced analytics, and Azure Data Lake Storage for efficient data management. Azure DevOps played a pivotal role in streamlining our CI/CD pipeline, ensuring that data flows and analytics models were always up to date. This tech stack enabled us to data-centric decision-making.

1. Scalability: Easily scale up or down to handle varying workloads and data volumes. 2. Cost Efficiency: Pay only for the resources you use, reducing infrastructure costs. 3. Data Accessibility: Access data from anywhere with internet connectivity. 4. Automatic Updates: Cloud providers handle software updates and maintenance. 5. Security: Benefit from robust security measures and compliance certifications.

Cloud-based data warehouses can be powerful tools, often providing data management capabilities. Tools that enable self-service-oriented data management can ensure integration with data engineering processes. This is key to stakeholder agility, and collaboration.

For building a consolidated report, we used to create a data warehouse and use ETL to extract and load the data. Many people still believe that this is required but can be moved to the cloud. Actually, the data doesn't have to be extracted separately, integrated in a data warehouse, before it can be consumed. It may be difficult to imagine that a single tool can extract, transform, and directly make the data become available in a report, but it does exist. The real problem is that investors won't believe that and encourage the market to go to a best of bread based approach. The cloud base data warehouse is backward, not forward. It is created by the force from the cloud vendors who would like to sell multiple services and then integrate.

Data lakes are designed to store massive volumes of raw data, allowing organizations to capture, store, and later process or analyze different types of data sources. Data lakes store unstructured data in its raw form, while data warehouses are more structured and optimized for analytics. Data lakes are often associated with the “schema-on-read” approach, meaning the schema is defined when data is read for analysis, not when it's ingested. This offers flexibility, as you can ingest data now and decide its structure later, unlike the more rigid “schema-on-write” approach of traditional data warehouses.

Moving to the cloud has simplified data infrastructure and data management in many ways so that users can take advantage of the data lake workload to achieve many of their goals. However, until recently, it was not feasible to build data applications off a data lake environment. However, Snowflake has overcome this.

Existing users of HIVE may want to consider Apache Iceberg. It is often used in the context of a data lakehouse architecture, where data is stored in its raw and structured form in a data lake, typically using columnar file formats like Parquet or ORC. Apache Iceberg can be used as a table format on top of these data lake storage systems, providing features like schema evolution, ACID transactions, data versioning, metadata management, and query optimization. This makes it easier to manage and query data in a structured and efficient manner within the data lakehouse. It helps bridge the gap between the traditionally separate worlds of data lakes and data warehouses, creating a unified platform for data-driven insights.

1. Unified Storage: Combine data lakes and data warehouses for structured and unstructured data. 2. Query Performance: Improve analytics by leveraging indexing and caching mechanisms. 3. Schema Evolution: Adapt schemas as data evolves, offering flexibility for changing needs. 4. Scalability: Scale horizontally to accommodate growing data volumes and concurrent users. 5. Cost Optimization: Achieve cost savings with serverless and pay-as-you-go models.

Data Lakehouse is taking best from data warehouse and data lake. Right now, I feel that this term is misleading and heavily used by vendors but doesn’t provide the value. It is clear that decouple storage and compute is the best approach now and every modern platform should lake house if it is not solving very particular narrow use case.

Emerging data warehouse technologies can help organizations to improve their data management by: Storing and analyzing all of their data in one place with a data lake. Combining the scalability and flexibility of a data lake with the performance and security of a data warehouse with a data lakehouse. Processing and analyzing data in real-time with a real-time data warehouse. Automating tasks such as data quality management and anomaly detection with machine learning (ML)-powered data warehouses.

In my experience every business wants insights immediately. They are supporting streaming 100%. But the. You are realized the typical data solution doesn’t need to be in real time, even batch every hour is not that important because most of the cases are about historical data trends, day over day, month over month and so on. I would avoid to use the streaming solution if there is no real business needs. But it is different in the vendor pitch, they are building solution for streaming, like Delta Streaming or Snowflake Dynamic tables and ready to charge you extra even if you don’t need it:)

Strange to see streaming data warehouse called out as a separate type of "warehouse". Your data lake, data warehouse, or data lakehouse could all have tables that are coming from streaming sources.

1. Continuous Data Ingestion: They support continuous data ingestion from various sources. 2. Low Latency: Provide near real-time analytics with minimal processing delays. 3. Event-Time Processing: Handle data based on event timestamps for accurate insights. 4. Complex Event Processing: Enable the analysis of complex event patterns and correlations. 5. Scalability: Scale horizontally to manage high data volumes and maintain low latency.

Streaming data saved my day, really! I was trying to build this IoT car during my university days and man, no matter how hard I tried, I was unable to calculate the average speed of the car until I discovered Dataflow in the Google Cloud Platform. It allowed me to analyze and query my streaming data in real time, using SQL or other languages. I was able to perform interactive or batch queries on my streaming data, as well as historical data. For example, I was able to see how the speed of the car varied over time, across different locations, or under different weather conditions. I was able to generate reports and dashboards on my streaming metrics, such as average speed, acceleration, fuel consumption, or emissions.

It's crucial to differentiate ???????? ???????????????????? from the above-mentioned data warehousing systems. While the latter requires physical data integration, data federation operates as an abstraction, a virtual layer, filling the gap between various data sources and users. Data Federation lets organizations establish secure, centralized, and accurate data management systems that often act as a dependable single source of truth for analysis and analytics workloads.

1. Data Virtualization: Provide a unified view of data without physical consolidation. 2. Source Diversity: Access data from diverse databases, cloud platforms, and formats. 3. Query Optimization: Optimize queries to fetch data efficiently from disparate sources. 4. Security and Privacy: Implement access controls to protect sensitive data across sources. 5. Scalability: Scale horizontally to accommodate additional data sources and evolving needs.

Actually, I think that Federated data warehouses will be the new standard in the industry. While our team requires data integration with other Data base or data warehouse, federated data warehouse will help us to simplify these troubles in architecture and resources. Data Federation gives us simplify, secure, centralized and accurate for all different steps of Data integration, data modeller and using this data for machine learning models or solutions for the business.

Federated data warehouses is a great step for your hybrid cloud environment. It enables seamless access to and analysis of data distributed across various data sources, including on-premises and cloud-based databases. It offers advantages such as data integration, cost efficiency, real-time access, and flexibility. They enable organizations to leverage the benefits of a distributed data ecosystem while maintaining control, consistency, and security in their data operations.

In some scenarios, such as an acquisition or a merger you need to join (usually bad!) data from two or more companies. In scenarios such as this, the federated data warehouse comes into its own, where organizations need to maintain distributed data sources and provide a unified view of the data without physically consolidating it into a 'one stop shop' for the data. These architectures are designed to seamlessly access and query data from multiple sources. They are also useful in data-rich industries such as media and entertainment marketing industry, for example, where federated data warehouses can integrate data from various sources such as content metadata, viewer engagement data, and advertising performance data.

1. Ontology Integration: Incorporate ontologies to define data relationships and meaning. 2. Semantic Annotations: Annotate data with semantic metadata for improved context. 3. Interoperability: Facilitate seamless integration of data from heterogeneous sources. 4. Semantic Queries: Enable semantic query languages for precise data retrieval. 5. Knowledge Graphs: Build knowledge graphs to represent complex data relationships.

In my opinion, knowledge graphs (I do not think semantic data warehouse as proposed here is a fitting term) are the way to go to connect large, often heterogeneous, and historically grown data silos. The data from the various sources can be ingested from relational (table) structures, semantically enriched, annotated, and even checked against predefined data and business rules (SHACL). Using virtual graphs, you can leave the data at the sources but still use graph-like queries. However, the best part is the strict standardization of the W3C. There is no vendor lock-in. The standards of data structure (RDFS, OWL), relational data ingestion (R2RML) query (SPARQL), and serialization (TTL, JSON-LD, n3) make all different systems interoperable.

Semantic data warehousing enhances the usability, accessibility, and quality of data in a data warehouse environment. It empowers organizations to make better use of their data assets, drive more informed decision-making, and streamline data-related processes.

I agree but, The best data warehousing solution will depend on a number of factors, such as the size and complexity of the dataset, the budget, and the specific needs of the business.

1. Data Governance: Establish data governance policies to ensure data quality, security, and compliance. 2. Data Privacy: Implement measures to protect sensitive and personal data in accordance with regulations. 3. Data Integration: Ensure compatibility and smooth integration with existing systems and data sources. 4. Scalability: Evaluate the system's ability to handle growing data volumes and increased user loads. 5. Total Cost of Ownership (TCO): Consider both initial setup costs and ongoing operational expenses. Taking these factors into account can help you make informed decisions when choosing and implementing data management technologies.

When choosing your architecture and technology, beware of the buzzwords! Also, be aware that the vendor may have sales and licensing targets to hit, so they may not advise in your best interests. Evaluating products and vendors for solutions is a critical process. It can significantly impact your organization's ability to manage, analyze, and derive insights from data effectively. A few points to consider: - Watch how they treat your team. Unfortunately, diversity and inclusion initiatives don't always land with some tech vendors, and they may diminish some of your team - define requirements - assess vendor finances - are they in trouble? - scalability - support - performance - reputation - ask for references - data governance - security

Data Maturity is a concept that is often forgotten in the industry. There is no value in just having the data, but rather using the data in the correct way. It typically progresses through stages, from data unawareness to data-driven excellence. As an organization matures, it becomes more proficient in collecting, storing, processing, analyzing, and deriving insights from its data. Data engineers play a crucial role in advancing an organization's data maturity. Data engineers ensure that data is collected, cleaned, transformed, and made available for analysis. They work to optimize data storage and access, making data more accessible and valuable for analysts and decision-makers.

For some cases you can consider to leverage completely Serverless approach, and use all benefits of cloud computing. For example, Athena, Synapse SQL serverless, Databricks Serverless and so on.