How can you speed up data queries and reports in business intelligence?

Data Engineers know that choosing the 'right' data source is tricky. It often starts with an assessment or interrogation of the data. Use the four V's of data to inform your business of the Who, What, When, Where, Why, and How? The four V's are Velocity, Veracity, Volume, and Variety. These are a great jumping-off point for understanding how your Data Sources will impact your Data Architecture. This helps you make the key decisions such as: - Who should see it? - What data are most important? - When you should index and on what columns/partitions? - Where data should live (Lakehouse, Warehouse, and/or Database) - Why are these data being used? - How does the data answer the question a user has proposed?

Often there is no choice in data sources. When this occurs an option is to consider going with ELT instead of ETL. ETL is often cumbersome, long to develop, and difficult to manage. If you land the data as-is then you can pick and choose what data is necessary to move on while still having it all available for future iterations.

Tuning to improve the response times for queries is a very interesting and complex work at time. There is no straight answer however there are multiple things that you could to find which among those or all will help improve the efficiency of queries. - Filter: Filter data at the earliest so you process data that is only relevant - Partitions: Use the right partitions such that the joins avoid full table scans - Statistics: Collect statistics frequently so that the processing engine can come up with best execution plans. - Indexing: Set up indexes on the correct columns - Caching: Load data into cache - Join Hints: Use join hints so that expensive join operations are avoided. There are many more options & some are specific to each tool

To expedite data queries and reports in business intelligence, several strategies can be employed. First, optimizing database performance by properly indexing tables, partitioning large datasets, and using in-memory databases can significantly enhance query speed. Additionally, leveraging caching mechanisms to store frequently accessed data can reduce query execution time for recurring requests. Employing parallel processing techniques, such as distributed computing or using multi-threaded queries, can also expedite data retrieval. Furthermore, optimizing the design of data models and queries by minimizing unnecessary joins, aggregations, and calculations can improve query performance

Need to spend time getting familiar with data source details. Profile the source data for primary keys, distribution and quality. Certain outliers and inline data transformations (division by zero, surprise!) can easily choke even a well designed query.

* Understand Your Data * Smart Indexing * Efficient Query Writing * Partitioning for Large Data * Compression and Caching * Regular Maintenance * Collaborate with Your Team I believe data optimization is about speed, efficiency, and teamwork!

In many large scale enterprises I have seen the usage of pre aggregated tables. These tables contain aggregated data for the corresponding reports for a given time interval. For example, a sales report might point to a table where total sales amount and other KPIs have already been calculated for last month, last quarter, last year etc. so that during report runtime there is no computational overhead and results are shown in sub-seconds.

A good example is when your queries aggregate large datasets of fine-grain data for reporting. A good approach is to provide the query with pre-aggregated data as a source for the query instead of aggregating the data during query run time. By moving data aggregation into the data model, queries run faster thus performance is optimized.

Write efficient queries, avoiding unnecessary joins and selecting only the necessary columns. Use stored procedures to precompile and optimize queries. Consider using query caching mechanisms if applicable. Aggregate data at the database level rather than in the BI tool when possible. Use summary tables or materialized views for frequently accessed aggregated data. Optimize the configuration of your BI tool, adjusting cache settings and memory allocation. Leverage the features provided by the BI tool for query optimization.

Data optimization transcends basic efficiency, involving intelligent indexing for rapid retrieval, strategic query design for minimal overhead, segmenting vast data for scalability, employing advanced compression and caching to boost speed, ensuring regular upkeep for sustained performance, and cultivating a collaborative environment to drive innovative data strategies, thereby enhancing operational agility and team synergy.

In traditional data warehouse, I have seen and implemented partition based approach. In most of the reporting queries we have audit date based filters, and queries are falling under different time buckets, so implemented a solution to create interval based auto partitions. It helped us to gain a lot of performance rather than scanning full table. Problem here was inter partition movement of the data rows that was acceptable as very less updates compared to data retrieval. Data purging also becomes faster in terms of whole partition than each row. Indexing on predicate columns always beneficial, Be careful on selecting indexing columns here. Pre - aggregated views also can be created to fasten report query execution time.

One often overlooked benefit of partitioning is data retention. Partitioning data in the right way let's you archive or remove data and keep the reporting performant - we're all guilty of holding on to data "just in case"!

Aside from partitioning and indexing, there are other data structuring methods that can improve query performance. These alternatives include sorting, organizing data into buckets based on hash or range criteria, or using Zindex. The choice between them depends on the specific situation and what works best for different types of tasks.

Let's say an e-commerce analytics platform aims to efficiently manage and analyze large volumes of sales and customer interaction data for business intelligence and reporting. Partition the sales_transactions table by transaction_date into yearly segments. Partition the customer_interactions table by interaction_date and create indexes on key columns like customer_id and region to enhance query performance. Implement an automated purging strategy to drop partitions older than two years, optimizing storage and performance. For reporting, use broadcast joins to quickly combine the large sales_transactions table with the smaller product_details lookup table, as well as with the customer_interactions table.

Query tuning is an art. I have seen tuning improve query performance 10 fold. I do generally avoid, and ask my developers to avoid, subqueries. CTEs are a much more readable format and depending on the database moving from a sub query to a CTE can also improve performance while improving maintainability.

Page load time is a metric that's used by the end users to know how much time an individual has to wait before all the visuals on the page are loaded. In order to optimize the KPI measures, variable caching can be used to store values which can be reused. This saves a good amount of time by avoiding recalculations. When multiple users view the report, it's essential to? perform load testing with concurrent users before it goes live. Try out an iterative / incremental refresh mechanism to avoid full data load.

Monitoring query performance should be a standard part of unit testing. If a developer creates a query and doesn't know how long it runs, and what the requirement is, then there is a bigger problem in development.

1.Data Modeling:Use star,snowflake or denormalised schema to optimise data access as per requirements.Verify appropriateness of data types, indexes,partition and use data compression. 2.Datawarehouse optimization:Use OLAP DW such as snowflake,Amazon redshift & google bigquery to handle large datasets and complex queries. 3.Query Optimization: Identify columns frequently used for joining tables implement indexing,usage of filters,subsumption of joins,semi joins,anti joins. 4.Implement result query cache or materialised views. 5.leverage parallel processing to distribute query workload across multiple nodes. 6.Remove obsolete data & have data retention policies. 7.Schedule data preprocessing to avoid impacting query usage during peak time

Ensure that data types in your fact table align with the nature of the data. Use appropriate numeric types and minimize storage requirements. Eliminate redundant or unnecessary columns to streamline data storage and retrieval. Opt for the most efficient join types based on the relationships between tables. Additionally, consider denormalization for frequently queried data. Employ advanced processing techniques such as materialized views to store summarized data. This minimizes the need for extensive calculations during query execution.

Reduce the latency, keep the data always closer to the business users otherwise, the data keeps traveling across networks leading to poor response times. Apply horizontal and vertical partition pruning : query response times can fine tuned by applying predicate push downs both ways. Use columnar databases, file formats to achieve partition pruning both ways.