3 Tools to Manage Data Warehouse Concurrency

A data warehouse is a repository for all the data that an organization obtains from many sources. It organizes this data into a single location where it can be accessed and analyzed to run the business. This data might come from numerous data streams, the Internet of Things, relational databases, and data management systems.

As the world becomes increasingly digitized, businesses must optimize their data warehouses to manage large volumes of data and extract knowledge from an inflow of information. Concurrency is a critical component of an integrated analytics environment that enables the best analytics functionalities to be delivered at scale.

Why Concurrency is Important

Data is essential for all businesses. They also need to find answers to difficult business problems that are concealed inside that data. It can be difficult to process large volumes of data in a data warehouse, especially when a company employs both traditional and cloud-based data warehouses.

Concurrency is essential for obtaining real-time business analytics at scale. It may not be difficult to manage ten users performing ten queries using ten rows or tables, but expanding to thousands or millions creates an environment that is impossible for humans to manage.

Concurrency guarantees that everyone uses the same real-time data warehouse without interfering with the work of others. This enables rapid innovation while also giving users complete trust in the accuracy of the data being used.

Transitioning from departmental point solutions to large-scale operational and production systems necessitates fine-tuned concurrency.

Tools for Managing Data Warehouse Concurrency

1. Firebolt

The primary motivations for most businesses to adopt cloud technologies have always been cost savings and access to cutting-edge technology. It is focused on leveraging recent innovations in analytics and the cloud to deliver much faster query performance and lower the total cost of ownership (TCO) at any scale.

Firebolt is the first third-generation cloud data warehouse, containing a variety of new features to boost performance and reduce TCO. These are some of its innovations:

• To enable high-performance access, Firebolt File Format (F3) storage, designed for network access, combines the benefits of columnar storage with support for continuous writes and native semi-structured data storage.

• It adds indexing to F3 to achieve sub-second speed. Sparse indexes with sorting and clustering are used to reduce the number of partitions and nodes in a query. Aggregating indexes are used to replace materialized views for aggregation operations. Joining indexing is used to speed up joins.

• It is a new, next-generation query engine that is optimized for multi-tasking. It provides features like vectorized processing, JIT compilation, cost-based optimization, indexing, and a slew of tuning options for better query speed and priority. It also allows numerous engines to run at the same time. Various users may be working on the same data in different ways.

2. Amazon Redshift

Amazon Redshift is a petabyte-scale cloud data warehouse platform for storing and analyzing large data sets that are completely managed. Large-scale database migrations are also performed with it.

The column-oriented database in Redshift is built to link to SQL-based clients and business intelligence tools, allowing users to access data in real-time. Redshift, which is based on PostgreSQL 8, provides quick speed and efficient querying to help teams make informed business choices.

Here are some of its features:

• Redshift scales like PostgreSQL. Within a single data warehouse, you can grow storage and queries. While replication (concurrency scaling) allows you to have up to ten identically-sized read-only copies of a warehouse, you can have one “master” warehouse for writing, which requires some amount of configuration and maintenance. New RA3 nodes from Redshift segregate storage and computing. Redshift Spectrum also allows federated querying, which includes pushdown to additional Spectrum nodes and destination data sources.

• Redshift supports up to 50 concurrent queries by default with concurrency scaling. This may work for internal analyst teams and conventional business intelligence but might not for operational or customer-facing analytics, which have considerably wider user groups.

3. Snowflake

Snowflake is a data warehouse in the cloud that operates on Amazon Web Services or Microsoft Azure. Because there is no hardware or software to pick, install, configure, or manage, it is ideal for businesses that don’t want to spend resources on the setup, maintenance, and support of in-house servers. Data may also be easily imported into Snowflake using an ETL tool like Stitch.

Some of its features are:

• Within a single warehouse, queries are scaled by allocating jobs to different nodes. However, for processes that require additional RAM, such as semi-structured data or sophisticated join procedures, one needs to expand a node’s capacity to select a larger warehouse. However, each level higher doubles the number of nodes and the cost.

• While multi-clustering keeps extra warehouses operating just when they’re needed, it’s costly because you have to pay for a whole new cluster to accommodate just one more user.

• In many benchmarks, Snowflake takes seconds to minutes to run a query for the first time, like for ad hoc queries. Because Snowflake caches data to local storage the first time it is read and employs a result cache to store all results, repetitive query workloads like reporting and dashboards may be quicker on average.

Summary

In a nutshell, various data warehouses are preferable for different scenarios. Hopefully, this information will assist you in selecting the appropriate data warehouse or query engine for various use cases. It may also assist you in preparing your analytics infrastructure to make these decisions.