In the digital age, where knowledge is power, it becomes inevitable for organizations to use data effectively. Businesses are collecting an ever-increasing amount of data, increasing the demand for dependable and efficient storage solutions. Companies store, organize, and properly analyze data using data lakes and warehouses.
Regardless of background – Business leadership, IT, or data enthusiast, this analysis will offer insightful knowledge on the transformative power of these technologies in depth.
Buckle up as we delve into real-world use case examples that demonstrate the revolutionary power of these technologies, as well as best practices that can assist organizations in realizing the full potential of their data.
5 Data Warehousing Best Practices That Work
Organizations rely more on data warehouses in the dynamic field of data management to convert unstructured data into meaningful insights. However, a data warehouse’s effectiveness depends on a strategic approach to make it work. Provided here are five data warehouse best practices.
- Defining clear business requirements
For the success of a data warehouse design, it is necessary to identify and specify the essential requirements of the organization. It involves interacting with business stakeholders to determine reporting requirements, data dependencies, and key performance indicators (KPIs). Ensuring that the data warehouse aligns with the company’s strategic goals is crucial to generate actionable insights.
- Optimized Data Modelling
A well-designed data model can handle the growing data load without compromising performance. Optimized data models can eliminate data redundancy. In addition to saving storage space and reducing duplication, it also aids in preserving the consistency and integrity of data.
- Ensuring Data Quality Management
The reliability of analytical results increases when the data is consistently clean and trustworthy. Guarantee the precision and dependability of the data and establish robust data quality control procedures. Establish standards for data quality and carry out regular audits to identify and address anomalies by processes including data cleaning, validation, and monitoring.
- Data Query Optimization
Ensure that queries operate effectively by paying attention to performance optimization. To enhance the speed of your database queries, consider utilizing partitioning, materialized views, and indexing. Analyze and track query performance regularly, looking for and fixing bottlenecks.
- Scalability and Flexibility
Consider scalability when designing the data warehouse. Select a scalable design that can accommodate growing user loads and data volumes. Cloud-based data warehouses offer on-demand resources and elastic scalability.
Examples include Azure Synapse, Google BigQuery, and Snowflake. Incorporate flexibility into the data warehouse to accommodate new data sources and adjust to evolving business needs.
5 Data Lake Best Practices That Work
A data lake enables consumers to extract information from relevant sources to support a range of analytical use cases and provide value to both technical and business teams. When data is stored and maintained following the best practices discussed below.
- Defining a Clear Data Lake Strategy
Clearly state the goals and objectives of the business that the data lake is supposed to assist. Understanding the kinds of analytics and insights the company hopes to obtain from the data is part of this.
Establish strong data governance policies and processes for guaranteed data security, compliance, and quality. Describe metadata management procedures, access restrictions, and data ownership.
- Security and access control
Put strong security measures in place to safeguard sensitive information stored in the data lake. Role-based access controls, data encryption, and unauthorized access monitoring are all included in this. To find and fix any vulnerabilities, regularly audit and verify security setups as a best practice when maintaining a data lake.
- Organize data effectively
Use a schema-on-read strategy to handle a variety of dynamic data sources with flexibility. Establish a robust metadata management system for data indexing and cataloging. It increases data accessibility and usability by assisting users in finding and comprehending the accessible data assets.
- Monitor performance tuning
Evaluate and improve the data lake’s performance regularly. It involves modifying resource allocations to fine-tune queries and optimizing data storage formats. Use logging and monitoring tools to check system performance and data usage.
- Enable Metadata Management
Organize each dataset’s metadata in a Data Lake by classification, labeling, and documenting it as part of a thorough metadata management strategy. Users will spend less time looking for appropriate data because it is easier to find and comprehend the material already available.
Use Cases of Data Lake
(1) Telecom: Content Delivery Networks generate log files that are huge in volume. It contains sensitive information about the performance of CDN servers and the quality of video streaming.
With data volumes reaching terabytes and data from various data sources, it posed a challenge for the client to manage this information in real-time and conduct analytics to grasp customer experiences and identify network issues.
GeakMinds solved it using real-time data ingestion using Spark, Kafka, and Anomaly Detection on the Microsoft Azure Platform. Data Lake is used for ingesting terabytes of data from various data sources. It helped our client fix issues in their CDN Network in near real-time and improve customer satisfaction.
(2) Banking: Consider an excerpt from McKinsey. A bank had been dealing with serious data issues, including an outdated data warehouse, data from voluminous sources, and a shortage of experts to handle various data sets arriving in varied formats.
In separate systems, crucial business data was frequently left unresolved with a delayed response.
In an attempt to facilitate the extraction, structuring, and transmission of data sets, they decided to opt for the data lake.
The bank expanded the use of the data lake to other areas in later months due to its success in high-profile business areas. Recording a back-end procedure only for utilized data instead of organizing all the data upfront was a significant change. The bank was able to eliminate data silos, allowing staff to access a variety of data sources (demographic, geographic, social media, and so on) to obtain an in-depth understanding of the company’s customers.
Use cases for Data Warehouse
(1) Telecom: GeakMinds helped to manage Text analysis for a Fortune 500 company. The client had to sift through thousands of contracts manually. It was a time-consuming process. The cloud-based Data Warehouse solution used was Azure Synapse Analytics. With Azure Cloud’s OCR and NLP techniques, clients can easily search specific contract clauses and verify invoices.
(2) Retail: Take a scenario where a retail organization runs several intricate Business Intelligence (BI) reports, regular queries during the day, and planned inquiries for the night. During nighttime, the company requires different computing capacity than during the day. To solve this issue, it puts up a minimal architecture that makes it simple to scale up or down the warehouse in response to workload. Any volume of data, users, and workloads may be handled by flexible scaling.
(3) E-Commerce: To effectively approach their clients, E-commerce platforms must collect vital marketing metrics (such as clicks, impressions, website traffic, etc.) from marketing tools. Here’s where data warehouses come in handy. Businesses can optimize performance by monitoring and displaying key metrics such as conversion rates, churn rates, return on ad spend, and secure data storage.
Conclusion
Firms must have the knowledge between data lakes and data warehouses and put best practices for their design and use into practice. Businesses must strive for a solid data architecture that supports effective data processing and storage. Companies can gain a competitive advantage by selecting appropriate data storage that suits their needs.