What are the best ways to handle data volume, variety, and velocity in your BI projects?

During a BI project, I faced challenges with massive transactional data volumes. I adopted a cloud-based solution for scalability and cost-efficiency. Implementing data compression, deduplication, and strategic partitioning streamlined our data management, enhancing analysis speed and accuracy. This experience underscored the need to proactively tackle data volume issues.

Data in itself maybe insignificant, however when you weave a story around the data it becomes invaluable. When we deal with large volumes of data, we need to keep in mind 3 mantras --> 1. Analyze business requirement & identify what data we need to meet the requirement. It is unwise to boil the ocean for our needs. 2. Spend time to analyze & avoid data redundancy & data quality issues. It is one of the top reasons for inefficient BI solutions 3. Create a semantic layer for data abstraction based on needs. This will improve the efficiency of most BI solutions.

I Think..The best way to handle data volume, variety, and velocity in business intelligence (BI) is to adopt a comprehensive approach. Utilizing technologies like big data platforms and cloud computing can effectively manage large volumes of diverse data at high speeds. Employing data governance practices and implementing real-time analytics tools can further optimize decision-making processes. Overall, a holistic strategy that combines robust technologies and efficient data management practices is crucial for successfully handling data in BI.

Use cloud-based platforms and services: Cloud-based platforms and services offer flexible and scalable storage and computing resources, which can help to address the challenges of data volume Use data compression and deduplication techniques: Data compression and deduplication techniques can reduce the size of data and eliminate redundant information Use data partitioning and indexing: Data partitioning and indexing can organize data into smaller units based on certain criteria; this can also improve data access and analysis efficiency

Alguns pontos que observo como importantes seja em uma arquitetura on-premise ou cloud em rela??o aos 3v's, s?o: Volume - Armazenamento escalável, com data aging Variedade - Tipo de armazenamento de objetos, isso garante a diversidade de formatos Velocidade - Dados armazenados num formato de arquivo de dados de código aberto orientado a colunas, por exemplo arquivo em formato parquet

Use a data warehouse: A data warehouse is a centralized repository that stores and integrates data from different sources. This can help to address the challenges of data variety Use a data lake: A data lake is a repository that stores data in its native format. This can help to address the challenges of unstructured and semi-structured data Use data virtualization: Data virtualization is a technology that allows you to access and query data from different sources without having to physically move the data. This can help to address the challenges of data variety and complexity

I trust that the first thing to think about is your data / static data. In other words, what do you want to achieve, what do you to get out of these, how do you want to represent, in other, know your data, before thinking a technical solution.

The best way to handle these 03 V's in BI projects is to configure the data pipelines via metadata approach. We can setup the full and incremental loads as well as the categorization of the data to cater the variety challenges.

Data Variety encompasses diverse data forms like text, images, videos, and more – akin to handling different types of mail. We adapt as we "read" them, without needing to know the content in advance. A robust data catalog acts as an index, integrating varied datasets, much like categorizing books by genre and content. ETL/ELT processes serve as translators, converting data into a common language for BI systems. Data virtualization creates a virtual library for seamless access to data from various sources. Handling data variety demands adaptability, organization, and the right tools, recognizing that data comes in many forms.

One of the toughest tasks is to unify all data sources in a single source of truth. There are many different challenges whether it is date time format, or data accent or lack of data dictionary.even then you might have UTC time zone issue. There are many data quality tools available to identify inconsistency

Many points already well covered. Additional consideration is to ensure that data is processing closer to the source of data generation. Often by the time insight is created, the value is lost. Having edge capabilities as part of overall platform ensures that processing happens near the source and insights are calculated near real time.

To achieve this, first delve into the intricacies of data workflows and the utilization of data in reporting: ?? Understanding the Workflow: Begin by scrutinizing the data entry process. How frequently is data inputted? Do you require change-tracking mechanisms? Is the data primarily for operational purposes or quality analysis? And how often will this data be accessed for reporting? Addressing these pivotal questions can significantly influence your data warehouse structure and the choice between ELT and ETL processes. ???To strike the perfect balance, consider maintaining a separate real-time data warehouse (i.e.hierarchical) tailored for operational reporting alongside an analytical data warehouse (i.e. lake, star-schema, relational).

To manage data velocity effectively: 1.Embrace real-time processing. 2.Ensure scalability of infrastructure. 3.Optimize data through compression. 4.Implement parallel processing. 5.Adopt event-driven architecture. 6.Consider Data Stream Management Systems (DSMS). 7.Utilize caching for improved response times. 8.Monitor metrics and set up alerts. 9.Establish robust data governance. 10.Continuously optimize data processing pipelines for adaptability and efficiency.

Use streaming analytics: Streaming analytics is a technology that allows you to process and analyze data as it is generated. This can help to address the challenges of data velocity Use in-memory computing: In-memory computing is a technology that stores and processes data in memory. This can help to improve the performance of BI queries Use real-time analytics: Real-time analytics is a technology that allows you to analyze data and generate insights in real time. This can help you to make faster and more informed decisions

The best way to handle data velocity is to use a data management solution that is designed to handle high-velocity data, such as a streaming data platform, a distributed database, a data warehouse, or a data lake. You can also improve your ability to handle data velocity by using data compression, partitioning, indexing, caching, and offloading

Data volume: 1. The amount of data that is generated and stored. 2. Data volume is like a snowball rolling downhill. It gets bigger and bigger as it goes. Data variety: 1. The different types of data that are generated and stored. 2. Data variety is like a puzzle. We need to put all the pieces together to get the full picture. Data velocity: 1.The speed at which data is generated and stored. 2. Data velocity is like a river. It's constantly flowing and changing. Data volume, variety, and velocity are the three pillars of big data. They are the foundation for many of the new technologies and innovations that are changing the world. By understanding and managing these three V's, we can unlock the full potential of big data.

Apart from Volume, Variety and Velocity, we also need to consider below important points: Data Quality, Scalability and Performance, Data Security and Compliance, Data Retention Policies So, whether you're a seasoned BI pro or just getting started, remember that BI is about managing data in smart and creative ways. We're all part of this community, and by sharing our experiences and knowledge, we can continue to grow and succeed together.

People! All the tooling, standards, methodologies and practices in the world won't help if you haven't got a quality team to implement them. Follow dataops principles and really work on creating an effective bi/data/development function and most other challenges won't be that much of a challenge any more.

When addressing the three main pillars of big data—volume, variety, and velocity—certain key considerations come into play: Volume Performance and Scalability: Efficient handling of large-scale data necessitates robust performance and scalability. Data Retention Policies Variety and Velocity Data Quality Data Lineage and Data Integrity: Maintaining data lineage and integrity is essential for preserving the data accuracy. Strategic Components Business Direction: Aligning data strategies with the business direction is fundamental for meaningful insights and decision-making. Data Governance: ensures quality, security, and usability of collected data. Master Data Management: maintain consistent and accurate core data across an organization.

Data Quality Assurance in Big Data for BI projects ensures accurate and reliable insights. It involves profiling, cleansing, validating, and enriching data. Continuous monitoring and improvement maintain data integrity, crucial for informed decision-making.