An Introduction to Online Analytical Processing (OLAP)

Dec 13, 2023
William To

Online analytical processing (OLAP) software executes detailed analysis on massive volumes of business data, drawn from sources such as data lakes or deep storage. 

End users, such as analysts, executives, and engineers, use OLAP platforms to dissect data around operational performance and profitability, access critical business insights, and steer business or product strategy.

The basics of OLAP

Online analytical processing arose to address a very important need. In order to improve and drive change, organizations need to make sense of their data—through analytics, aggregation, and other operations. 

However, any OLAP software has to overcome several important challenges.

Dispersed data. Due to the diversity of data sources and storage, OLAP systems have to gather and analyze data from various locations. This requires compatibility with a wide range of products, such as data lakes, connectors, streaming data technologies, and more.

Data input via ETL, streaming, and more. When data lives on decentralized storage, an OLAP solution needs to extract data, transform it into a compatible format or structure, and load it, a process known as ETL (more on this later). While this process is still being used, many OLAP systems can also use streaming for data intake, ingesting real-time data events via Amazon Kinesis or Apache Kafka.

Scale. By nature, OLAP architectures are designed to handle large amounts of data. For example, a quarterly report for a multinational corporation will likely involve sifting through terabytes of data. As a result, some OLAP products will include features such as ROLLUP to help compress vast volumes of data into something more manageable.

Resource-intensive operations. Many common OLAP operations, such as drilling down, slicing and dicing, or pivoting, require a lot of computing power. For this reason, OLAP tools have to use parallel processing to accommodate a large number of concurrent users and simultaneous, complex queries.

Speed. Traditionally, OLAP use cases were not time sensitive—quarterly reports, for instance, generally don’t require rapid turnaround times. However, in today’s fast-paced world, those organizations which can rapidly analyze and act on data have an edge over slower competitors, whether it’s a game studio fixing a faulty feature or a digital agency shifting their ad targeting. However, because real-time analytics is still an emerging market, few OLAP solutions are optimized for its demands.

The difference between OLAP and OLTP

For the most part, databases can be divided into two categories: Online Analytical Processing, or OLAP, and Online Transactional Processing, or OLTP. Traditionally, the boundary between the two is clear: whereas OLAP software executes in-depth data analysis to provide business intelligence, OLTP systems handle the day-to-day operations of an organization. Due to their different roles, OLAP databases often work with historical data, while OLTP databases are optimized for real-time data (though this distinction is beginning to blur).

As an analogy, an OLAP system is akin to fitness monitoring for companies. A user can check the health of their organization, see where they excel or fail, devise plans to improve performance, and assess the success of their plans. An OLTP database is the tool that actually implements these strategies, pulling data for transactions, storing records, and verifying interactions between the company and its customers.

This means that OLTP databases can read, write, and delete data rapidly, crucial for meeting real-time demands. In addition, OLTP databases are Atomic, Consistent, Isolated, and Durable, or ACID compliant: basically, transactions are indivisible, discrete operations that do not negatively impact query concurrency or data consistency, and are protected against data loss. This is extremely important for OLTP databases, because many transactions, such as credit card purchases or stock trading, are high volume, fast-paced, and high stakes.

Transactional databases also function differently than analytical ones. Because their data is needed immediately, transactional databases are optimized for lots of rapid, shallow operations. Think of an online retailer retrieving a shipping address for a purchase, or an airline’s automated booking system confirming that connecting times are long enough to permit transfers. In both situations, quick data access is necessary to complete the operation and earn revenue.

Issues arise when OLTP databases are used to perform OLAP duties, or vice versa. In the former case, OLTP software simply doesn’t have the processing power, architecture, or data structures to efficiently execute complex operations like slicing or drilling down, at least not quickly—and not on vast volumes of data. Similarly, OLAP databases cannot carry out lots of fast, small operations involving many parallel users and queries.

Today, the divide between OLTP and OLAP databases remains. However, the rise of real-time analytics, which requires both the ability to accommodate large numbers of concurrent users as well swiftly return results for increasingly complicated queries, is upending this long-running paradigm.

How OLAP databases work

At the most basic level, OLAP systems absorb data from across an environment, transform it into the same format or data type for easier access, and organize this data to ensure fast queries. 

After a data event is generated, it can be stored in a variety of places, including transactional databases like MongoDB or PostgreSQL, or unstructured storage (also known as data lakes) like Amazon S3 or Hadoop. 

For example, a retail point of sale (POS) tablet might generate an event such as a sale, which would live in a transactional database for quick retrieval. After a set period of time, this event would then be automatically moved to a data lake for cold storage, freeing up space in the transactional database for more recent events.

From there, an OLAP product can use any number of ways to ingest data. Some OLAP solutions optimized for rapid, real-time analytics can stream data into their system, either directly from the source (such as a sensor) or via a stream processor (such as Apache Flink), which converts raw event streams into a more digestible configuration.

Alternatively, an OLAP system can ETL the data from a data lake or transactional database. After extracting the data using a connector or other intermediary, the OLAP database would transform it, or prepare the data for analysis by stripping out duplicate readings, filling in missing values, translating it into a different format, and structuring it in columns or tables.

In addition, some OLAP databases ELT their data, extracting and loading it into memory before any transformations. While ETL is often used for large quantities of data, ELT is preferred for smaller data pipelines or environments. 

After data has been imported and added to an OLAP product, it is now ready to be analyzed. One way to do so is the OLAP cube, which despite its name, isn’t necessarily a cube—or even a 3D object. Instead, an OLAP cube is a framework for collecting data across different dimensions and aggregating it for exploration and analysis. 

By including all the relevant data in a single structure, an OLAP system can ensure ease of access, and fast queries, as well as facilitating operations like slice-and-dice, filtering, and more. As a result, OLAP cubes can be more intuitive to use, especially for less-technical employees, who don’t have to write lines of code, SQL statements, or JOIN data tables. The downside is that new reports may require a new cube, as adding too many dimensions to a single cube may risk confusion.

Let’s take the example of an analyst for an outdoor retailer, who’s compiling a yearly sales report. To assemble their cube, this analyst might start by determining the dimensions required, such as the highest selling product categories across their entire business (cookware, tents, compasses, and jackets), the territories in which they sold most successfully, and sales figures by quarters. 

Each dimension would become a separate axis, so that the final cube would be configured in a manner similar to this illustration. 

As mentioned above, cubes may have more or less than three axes. If the analyst in this example needs to look at more data, they can do so—adding in dimensions like year-over-year change, buyer demographics such as household income, or even disaggregating each product category into multiple brands.

After data is inputted into a cube, this analyst can run the five core operations featured in all OLAP software:

  1. Rollup. By reducing the number of rows or dimensions, rollup compresses and summarizes data, thus saving storage space and reducing costs. Rollup is ideal for situations where high-cardinality values are either scarce or not necessary, or where data granularity can be lengthened (such as setting intervals to one minute instead of one hour). Otherwise, there are a number of ways to compact data without losing detail, such as using sketches to approximate high-cardinality data, rather than using the exact value. To return to our sample e-commerce store, an analyst could rollup sales figures from cities to countries, or from quarters to halves.
  1. Drill down. In contrast to rollup, drill down creates more detail and granularity from data. In the previous e-commerce example, the analyst could drill down into their quarterly sales figures to uncover sales revenues by week or month, regional sales figures by city, or best-selling products by brand.
  1. Slice. As the name implies, slicing essentially dissects the OLAP cube along a single dimension to provide more specifics. For instance, the e-commerce analyst could slice the OLAP cube to discover the yearly revenue for an individual store in Portland, Oregon. 
  1. Dice. Unlike slicing, dicing separates two or more OLAP cube dimensions for in-depth comparisons. As an example, the analyst could compare the yearly revenue and most popular products of the Portland store against that of the Denver store, in order to assess if different goods are more successful in different geographies.
  1. Pivot. By rotating the cube on its axis, users can get a new perspective on their data. As an example, instead of looking at products popular in Portland and Denver, the analyst can spin the diced OLAP cube to determine which store had more sales overall.

The different types of OLAP databases

Today, there are three types of OLAP systems: Relational (ROLAP), Multidimensional (MOLAP), and Hybrid (HOLAP).

Multidimensional OLAP databases utilize datacubes for multidimensional analysis and exploration. As a result, MOLAP-type databases excel at complicated analysis, especially the five core OLAP operations, and don’t require business users to possess a high level of technical knowledge. Due to its design, which heavily emphasizes pre-aggregations and indexing to better organize data, MOLAP databases can often rapidly return analytical queries.

However, pre-aggregations require considerable storage space, which may cause MOLAP solutions to encounter difficulties when faced with large datasets. Further, MOLAP datacubes also require preconfigured dimensions, which necessitates that users know exactly what kinds of queries and data they’ll be looking for. In addition, datacubes may also slow down when significant numbers of dimensions are introduced.  

Relational OLAP databases, unlike MOLAP ones, do not rely on pre-aggregation to structure data. Instead, they organize data in tables and relationships, much like traditional relational databases, and require operations like JOINs to merge data from multiple places.

Unlike MOLAP, ROLAP also performs aggregations as they are generated, rather than pre-aggregating data ahead of time. While this approach does not require the large storage footprint of MOLAP pre-aggregations, executing operations on demand can lead to slower performance, especially when large quantities of data are involved.

Hybrid OLAP software takes the best of both the ROLAP and MOLAP models, storing some data in OLAP cubes for fast, pre-aggregated queries while retaining the remainder of the data in a relational data architecture. The use of this relational data store enables the storage of more granular data, as well as better scalability than a pure MOLAP database. 

Ultimately, a HOLAP data solution provides a flexible experience, keeping the user-friendly nature and fast multidimensional queries of datacubes without sacrificing the ability to accommodate large volumes of data. It also ensures that users don’t have to choose between fine-grained data or high-level insights, instead allowing users to switch between the two types of data as needed.

The enduring popularity of OLAP databases

While OLAP as a term was invented in 1993 by famed computer scientist Edgar F. Codd, this technology has remained popular because it offers something to everyone at all levels of an organization. 

For users, such products were both intuitive and powerful: even those without a deep background in coding or SQL could utilize OLAP software to extract important conclusions from massive volumes of data. As a result, analysts no longer had to rely heavily on engineers or database administrators to compile reports—leading to more streamlined processes involving fewer parties, and ultimately, faster results.

For decision makers, OLAP provided invaluable insights for assessing and improving their company’s operations and business model. Armed with metrics like customer churn, product revenue for specific regions or quarters, and growth across a business unit, managers and vice presidents could chart out new strategies, abandon underperforming initiatives, or pivot to address changing market conditions.

For employees and executives alike, OLAP systems also offer the advantage of breaking down the organizational barriers (silos) that naturally arise between different departments. By ingesting and analyzing data from across the company, any adequate OLAP solution can provide an accurate, top-down view of performance, complete with minute details. 

One example could be a vertically integrated energy conglomerate, which through its subsidiaries, controls every step of the solar panel process, from mining raw materials to manufacturing and operating the finished product. In the past, much of this data, including mine productivity, delivery and production times, or panel performance, could have easily been lost in the shuffle between various offices. 

However, an OLAP database could provide a unified view into performance and identify important patterns. An executive may notice that specific markets, such as resource-scarce islands forced to import natural gas, are seeing an uptick in solar panel adoption. To capitalize on this opportunity, the executive could decide to open up more sales offices in these areas, provide more budget for marketing and outreach, and train local installers and maintainers. 

Further, as data has grown in volume, speed, and diversity, the OLAP ecosystem has also evolved to keep pace and become vastly more capable, especially in contrast to early offerings. Today, there are hundreds of OLAP databases on the market with a dizzying array of specializations—being used for finance, marketing, manufacturing, energy, and many more industries. 

Alongside their core aggregations, many of these new OLAP products also offer advanced features, including interactive visualizations, forecasting, trendspotting, and specialized capabilities for use cases such as timeseries or IoT sensor data. Many have also moved onto the cloud, leveraging its scalability, reduced costs, and improved operational efficiency.

Data modeling for OLAP

Effective data modeling can enable fast queries, smooth operations, and intuitive user experiences. A schema is a blueprint for a database, describing the underlying structure of the data stored within, as well as providing loose guidelines and best practices around implementation.

In the context of OLAP, there are three main types of schema utilized today:

  1. Star. As the name suggests, star schemas are rooted in a single, centrally located data table, from which extends many branch tables, each of which contains a dimension. Star tables essentially model one-to-many relationships, JOINing the peripheral dimension tables with the central data table. As a result, these operations are somewhat simpler (and faster) than in other database schemas, and because of this, star schemas are often the underlying structure for datacubes. 

Unfortunately, the general structure of the star schema can encourage redundancy in branch tables, leading to a larger storage footprint (and higher costs). In addition, star schemas can be challenging to alter; as they are denormalized, certain commands like upserts or inserts can introduce inconsistencies to data. In a similar vein, OLAP databases utilizing star schemas are less scalable and not as efficient with storage. 

  1. Snowflake. Built to address many of the shortcomings of the star schema, snowflake schemas follow a similar format, with dimension tables clustered around a central data table. The key differences are that snowflake schemas normalize data by subdividing the ancillary dimension tables so that they each have their own branch tables. (The resulting pattern resembles a snowflake, hence the name). 

Snowflake schemas do have some advantages, specifically data normalization and storage efficiency. Normalization simplifies many routine operations, such as updates, inserts, and deletions. It also utilizes less disk space, simply because there is little duplicated data. 

Unfortunately, the elaborate structure of snowflake schemas also has drawbacks, namely in processing efficiency. Because of the layers of subdivided tables, query execution can be complex and slower than expected. Snowflake schemas are also harder to design and maintain.

  1. Galaxy. Also known as constellations, galaxy schemas are another variation of star schemas. Consisting of multiple fact tables, each linked to a host of ancillary dimension tables, the structure of this schema heavily resembles clusters of planets orbiting around stars—hence the name. 

The advantages of this format include excellent flexibility, better modeling for complex datasets and relationships, and more support for multidimensional data operations. Like snowflake schemas, galaxies are normalized, which reduces data redundancy and storage footprints and costs.

Still, galaxy schemas have some drawbacks. Because galaxy schemas have multiple fact tables, their dimension tables are subdivided only once, as more subdivisions would become too complicated and confusing. In addition, querying data stored in galaxy schemas can be slower than equivalent queries in star and snowflake schemas, simply due to sheer complexity.

Use cases for OLAP

Thanks to its processing capabilities and relevance, OLAP is everywhere. In fact, there likely isn’t a sector that has not been transformed by the adoption of OLAP technologies. 

Observability. Maintaining, improving, and troubleshooting digital infrastructure relies on significant amounts of data. Read more about an observability use case here

Telecommunications. Analysts can use OLAP to better understand network performance, subscriber behavior, and telecommunications service quality. Read more about a telecommunications use case here.

Retail. OLAP databases play a big role in analyzing customer behavior, product success, and revenue. Teams can also better plan retail strategy going forward with data.

Utilities and energy. Customer consumption data can be ingested and analyzed to determine patterns, forecast future usage, and inform grid improvements or energy bids. Read more about an energy use case here.


Online analytical processing (OLAP) empowers organizations and teams to analyze their data, understand their performance and operations, and make changes—whether it’s improving existing products, abandoning unsuccessful courses of action, or pivoting to new strategies. In order to do so, OLAP databases must gather, organize, and query vast amounts of data.

First, data is ingested from a variety of sources, such as transactional databases, unstructured data storage, or data lakes. Then, this data is transformed into a suitable data schema, such as star, snowflake, or galaxy, which provides structure and facilitates the five core OLAP queries: rollup, drill down, slice, dice, and pivot, which provide different perspectives on data.

While there are various types of OLAP databases—such as Multidimensional OLAP, Relational OLAP, and Hybrid OLAP—these products are all intended for similar goals. MOLAP databases organize data into data cubes for analysis, ROLAP databases are arranged much like SQL databases, and HOLAP combines the best of both types.

Why use Apache Druid for OLAP? 

As the database for speed, scale, and streaming data, Apache Druid was created as an OLAP solution that can merge the best of both worlds: the fast response times and concurrent support of transactional databases, alongside the complex operations and massive scale of analytical databases. 

Druid is also easily scaled up or down, as query, storage, and cluster coordination functions are devolved across separate node types that can be added or removed on demand. Afterwards, data and workloads are automatically rebalanced or retrieved from deep storage and redistributed throughout nodes.

Deep storage also maintains resilience and reliability by serving as a continuous backup and emergency data store. Should a node fail, its data can be accessed from deep storage and allocated onto any remaining (or restored) nodes. 

Druid is natively compatible with two of the most common streaming platforms, Amazon Kinesis and Apache Kafka, enabling it to ingest data without any additional workarounds or connectors. Data is ingested exactly once, ensuring that no events are duplicated, and are instantly made available for querying and data analysis.

Druid can autodetect schema, change tables accordingly, and avoid any downtime in the process. In contrast, other databases require tables to be manually altered whenever schema is changed, an operation that can take the entire database offline for hours as updates run. 

Because data can change on a day-to-day basis, this usually results in data with different fields—data collected by devices with up-to-date software may have different fields that older data is missing, and vice versa. However, Druid’s schema autodetection features will ensure that fields remain consistent across all data (older tables will simply have a NULL value where they’re missing this information).

Along with open source Apache Druid, Imply also features paid products including Polaris, the Druid database-as-a-service—and the easiest way to get started with Druid. Another popular product is Pivot, an intuitive GUI that simplifies the creation of rich, interactive visualizations and dashboards—for both external and internal use.

To learn more about Druid, read our architecture guide
To learn more about real-time analytics, request a free demo of Imply Polaris, the Apache Druid database-as-a-service, or watch this webinar.

Other blogs you might find interesting

No records found...
Feb 21, 2024

What’s new in Imply Polaris – January 2024

At Imply, we're excited to share the latest enhancements in Imply Polaris, our real-time analytics Database-as-a-Service (DBaaS) powered by Apache Druid®. Our commitment to refining your experience with Polaris...

Learn More
Feb 21, 2024

Introducing Apache Druid 29.0

Apache Druid® is an open-source distributed database designed for real-time analytics at scale. We are excited to announce the release of Apache Druid 29.0. This release contains over 350 commits & 67 contributors.

Learn More
Feb 14, 2024

Apache Druid vs. ClickHouse

If your project needs a real-time analytics database that provides subsecond performance at scale you should consider both Apache Druid and ClickHouse. Find out how to make an informed choice.

Learn More
Jan 23, 2024

Enhancing Data Security with Role-Based Access Control in Druid and Imply

Managing user access to relevant data is a crucial aspect of any data platform. In a typical Role Based Access Control (RBAC) setup, users are assigned roles that determine their access to relevant data. We...

Learn More
Jan 16, 2024

Comparing Data Formats for Analytics: Parquet, Iceberg, and Druid Segments

In this blog, I will give you a detailed overview of each choice. We will cover key features, benefits, defining characteristics, and provide a table comparing the file formats. Dive in and explore the characteristics...

Learn More
Jan 12, 2024

Scheduling batch ingestion with Apache Airflow

This guide is your map to navigating the confluence of Airflow and Druid for smooth batch ingestion. We'll get you started by showing you how to setup Airflow and the Druid Provider and use it to ingest some...

Learn More
Dec 29, 2023

A Buyer’s Guide to OLAP Tools

How do OLAP databases work—and which one is right for you? Read this blog post to learn more about which OLAP solutions are best for different use cases.

Learn More
Dec 26, 2023

What is IoT Analytics?

Because it deals with fast-moving, real-time data, IoT analytics is uniquely challenging. Learn how to overcome these challenges and how to extract (and act on) valuable insights from IoT data.

Learn More
Dec 19, 2023

OLTP and OLAP Databases: How They Differ and Where to Use Them

Learn about the differences between analytical and transactional databases—their strengths and weaknesses, what they’re used for, and which option to choose for your own use case.

Learn More
Dec 15, 2023

Query from deep storage: Introducing a new performance tier in Apache Druid

Now, Druid offers a simpler, cost-effective solution with its new feature, Query from Deep Storage. This feature enables you to query Druid’s deep storage layer directly without having to preload all of your...

Learn More
Dec 15, 2023

How KakaoBank Uses Imply for Financial Analysis

As a mobile-first digital platform, KakaoBank accumulates a substantial amount of data. Therefore, analysts need a solution that can effectively analyze and pre-process large quantities of data, visualize the...

Learn More
Dec 14, 2023

Joins, Multi-Stage Queries, and More: Relive the Excitement of Druid Summit 2023

Druid Summit kicked off its fourth year as a global gathering of minds passionate about real-time analytics and the power of Apache Druid. This year’s event revealed a common theme: the growing significance...

Learn More
Dec 12, 2023

Real-Time Data: What it is, Why it Matters, and More

Real-time data travels directly from the source to end users, so that it can be processed and acted on instantly. Learn all about the challenges, benefits, and best practices for real-time data.

Learn More
Dec 08, 2023

Druid vs Pinot: Choosing the best database for Real-Time Analytics

Do you want fast analytics, with subsecond queries, high concurrency, and combination of streams and batch data? If so, you want real-time analytics, and you probably want to consider the two Apache Software...

Learn More
Dec 07, 2023

What’s new in Imply Polaris – October and November 2023

At Imply, our commitment to continually improving your experience with Imply Polaris—our real-time analytics Database-as-a-Service (DBaaS) powered by Apache Druid®—is evident in recent developments. Over...

Learn More
Nov 15, 2023

Introducing Apache Druid 28.0.0

Apache Druid 28.0, an open-source database for real-time analytics, introduces Async queries, UNION ALL support, SQL WINDOW functions, enhanced ingestion features, including multi-Kafka topic support, and...

Learn More
Oct 18, 2023

Migrating Data From S3 To Apache Druid

This blog covers the rationale, advantages, and step-by-step process for data transfer from AWS s3 to Apache Druid for faster real-time analytics and querying.

Learn More
Oct 12, 2023

What’s new in Imply Polaris, our real-time analytics DBaaS  – September 2023

Every week, we add new features and capabilities to Imply Polaris. Throughout September, we've focused on enhancing your experience as you explore trials, navigate data integration, oversee data management,...

Learn More
Sep 27, 2023

Introducing incremental encoding for Apache Druid dictionary encoded columns

In this blog post we deep dive on a recent engineering effort: incremental encoding of STRING columns. In preliminary testing, it has shown to be quite promising at significantly reducing the size of segment...

Learn More
Sep 21, 2023

Migrate Analytics Data from MongoDB to Apache Druid

This blog presents a concise guide on migrating data from MongoDB to Druid. It includes Python scripts to extract data from MongoDB, save it as CSV, and then ingest it into Druid. It also touches on maintaining...

Learn More
Sep 21, 2023

How Druid Facilitates Real-Time Analytics for Mass Transit

Mass transit plays a key role in reimagining life in a warmer, more densely populated world. Learn how Apache Druid helps power data and analytics for mass transit.

Learn More
Sep 19, 2023

Migrate Analytics Data from Snowflake to Apache Druid

This blog outlines the steps needed to migrate data from Snowflake to Apache Druid, a platform designed for high-performance analytical queries. The article covers the migration process, including Python scripts...

Learn More
Sep 15, 2023

Apache Kafka, Flink, and Druid: Open Source Essentials for Real-Time Data Applications

Apache Kafka, Flink, and Druid, when used together, create a real-time data architecture that eliminates all these wait states. In this blog post, we’ll explore how the combination of these tools enables...

Learn More
Sep 11, 2023

Visualizing Data in Apache Druid with the Plotly Python Library

In today's data-driven world, making sense of vast datasets can be a daunting task. Visualizing this data can transform complicated patterns into actionable insights. This blog delves into the utilization of...

Learn More
Sep 05, 2023

Bringing Real-Time Data to Solar Power with Apache Druid

In a rapidly warming world, solar power is critical for decarbonization. Learn how Apache Druid empowers a solar equipment manufacturer to provide real-time data to users, from utility plant operators to homeowners

Learn More
Sep 05, 2023

When to Build (Versus Buy) an Observability Application

Observability is the key to software reliability. Here’s how to decide whether to build or buy your own solution—and why Apache Druid is a popular database for real-time observability

Learn More
Aug 29, 2023

How Innowatts Simplifies Utility Management with Apache Druid

Data is a key driver of progress and innovation in all aspects of our society and economy. By bringing digital data to physical hardware, the Internet of Things (IoT) bridges the gap between the online and...

Learn More
Aug 14, 2023

Three Ways to Use Apache Druid for Machine Learning Workflows

An excellent addition to any machine learning environment, Apache Druid® can facilitate analytics, streamline monitoring, and add real-time data to operations and training

Learn More
Aug 11, 2023

Introducing Apache Druid 27.0.0

Apache Druid® is an open-source distributed database designed for real-time analytics at scale. Apache Druid 27.0 contains over 350 commits & 46 contributors. This release's focus is on stability and scaling...

Learn More
Aug 10, 2023

Unleashing Real-Time Analytics in APJ: Introducing Imply Polaris on AWS AP-South-1

Imply, the company founded by the original creators of Apache Druid, has exciting news for developers in India seeking to build real-time analytics applications. Introducing Imply Polaris, a powerful database-as-a-Service...

Learn More
Aug 03, 2023

Embedding Visualizations using React and Express

In this guide, we will walk you through creating a very simple web app that shows a different embedded chart for each user selected from a drop-down. While this example is simple it highlights the possibilities...

Learn More
Jul 25, 2023

Apache Druid: Making 1000+ QPS for Analytics Look Easy

This 2-part blog post explores key technical considerations to support high QPS for analytics and the strengths of Apache Druid

Learn More
Jul 25, 2023

Things to Consider When Scaling Analytics for High QPS

This 2-part blog post explores key technical considerations to support high QPS for analytics and the strengths of Apache Druid

Learn More
Jul 20, 2023

Automate Streaming Data Ingestion with Kafka and Druid

In this blog post, we explore the integration of Kafka and Druid for data stream management and analysis, emphasizing automatic topic detection and ingestion. We delve into the creation of 'Ingestion Spec',...

Learn More
Jul 12, 2023

Schema Auto-Discovery with Apache Druid

This guide explores configuring Apache Druid to receive Kafka streaming messages. To demonstrate Druid's game-changing automatic schema discovery. Using a real-world scenario where data changes are handled...

Learn More
Jul 11, 2023

What’s new in Imply Polaris – Q2 2023

Imply Polaris, our ever-evolving Database-as-a-Service, recently focused on global expansion, enhanced security, and improved data handling and visualization. This fully managed cloud service, based on Apache...

Learn More
Jun 06, 2023

Introducing hands-on developer tutorials for Apache Druid

The objective of this blog is to introduce the new set of interactive tutorials focused on the Druid API fundamentals. These tutorials are available as Jupyter Notebooks and can be downloaded as a Docker container.

Learn More
Jun 01, 2023

Introducing Schema Auto-Discovery in Apache Druid

In this blog article I’ll unpack schema auto-discovery, a new feature now available in Druid 26.0, that enables Druid to automatically discover data fields and data types and update tables to match changing...

Learn More
May 30, 2023

Exploring Unnest in Druid

Druid now has a new function, Unnest. Unnest explodes an array into individual elements. This blog contains design methodology and examples for this new Unnest function both from native and SQL binding perspectives.

Learn More
May 28, 2023

What’s new in Imply Polaris – Our Real-Time Analytics DBaaS

Every week we add new features and capabilities to Imply Polaris. This month, we’ve expanded security capabilities, added new query functionality, and made it easier to monitor your service with your preferred...

Learn More
May 24, 2023

Introducing Apache Druid 26.0

Apache Druid® 26.0, an open-source distributed database for real-time analytics, has seen significant improvements with 411 new commits, a 40% increase from version 25.0. The expanded contributor base of 60...

Learn More
May 22, 2023

ACID and Apache Druid

ACID and Druid, an interesting dive into some of the Druid capabilities in the light of ACID compliance

Learn More
May 21, 2023

How to Build a Sentiment Analysis Application with ChatGPT and Druid

Leveraging ChatGPT for sentiment analysis, when combined with Apache Druid, offers results from large data volumes. This integration is easily achievable, revealing valuable insights and trends for businesses...

Learn More
May 21, 2023

Snowflake and Apache Druid

In this blog, we will compare Snowflake and Druid. It is important to note that reporting data warehouses and real-time analytics databases are different domains. Choosing the right tool for your specific requirements...

Learn More
May 20, 2023

Learn how to achieve sub-second responses with Apache Druid

Learn how to achieve sub-second responses with Apache Druid. This article is an in-depth look at how Druid resolves queries and describes data modeling techniques that improve performance.

Learn More
May 19, 2023

Apache Druid – Recovering Dropped Segments

Apache Druid uses load rules to manage the ageing of segments from one historical tier to another and finally to purge old segments from the cluster. In this article, we’ll show what happens when you make...

Learn More
May 18, 2023

Real-Time Analytics: Building Blocks and Architecture

This blog identifies the key technical considerations for real-time analytics. It answers what is the right data architecture and why. It spotlights the technologies used at Confluent, Reddit, Target and 1000s...

Learn More
May 17, 2023

Transactions Come and Go, but Events are Forever

For decades, analytics has focused on Transactions. While Transactions are still important, the future of analytics is understanding Events.

Learn More
May 16, 2023

What’s new in Imply Polaris – Our Real-Time Analytics DBaaS

This blog explains some of the new features, functionality and connectivity added to Imply Polaris over the last two months. We've expanded ingestion capabilities, simplified operations and increased reliability...

Learn More
May 15, 2023

Elasticsearch and Druid

This blog will help you understand what Elasticsearch and Druid do well and will help you decide whether you need one or both to reach your goals

Learn More
May 14, 2023

Wow, that was easy – Up and running with Apache Druid

The objective of this blog is to provide a step-by-step guide on setting up Druid locally, including the use of SQL ingestion for importing data and executing analytical queries.

Learn More
May 13, 2023

Top 7 Questions about Kafka and Druid

Read on to learn more about common questions and answers about using Kafka with Druid.

Learn More
May 12, 2023

Tales at Scale Podcast Kicks off with the Apache Druid Origin Story

Tales at Scale cracks open the world of analytics projects and shares stories from developers and engineers who are building analytics applications or working within the real-time data space. One of the key...

Learn More
May 11, 2023

Real-time Analytics Database uses partitioning and pruning to achieve its legendary performance

Apache Druid uses partitioning (splitting data) and pruning (selecting subset of data) to achieve its legendary performance. Learn how to use the CLUSTERED BY clause during ingestion for performance and high...

Learn More
May 10, 2023

Easily embed analytics into your own apps with Imply’s DBaaS

This blog explains how developers can leverage Imply Polaris to embed robust visualization options directly into their own applications without them having to build a UI. This is super important because consuming...

Learn More
May 09, 2023

Building an Event Analytics Pipeline with Confluent Cloud and Imply’s real time DBaaS, Polaris

Learn how to set up a pipeline that generates a simulated clickstream event stream and sends it to Confluent Cloud, processes the raw clickstream data using managed ksqlDB in Confluent Cloud, delivers the processed...

Learn More
May 08, 2023

Real time DBaaS comes to Europe

We are excited to announce the availability of Imply Polaris in Europe, specifically in AWS eu-central-1 region based in Frankfurt. Since its launch in March 2022, Imply Polaris, the fully managed Database-as-a-Service...

Learn More
May 07, 2023

Stream big, think bigger—Analyze streaming data at scale in 2023

Imply is predicting the next "big thing" in 2023 will be analyzing streaming data in real time (and Druid is built for just that!)

Learn More
May 07, 2023

Should You Build or Buy Security Analytics for SecOps?

When should you build—or buy—a security analytics platform for your environment? Here are some common considerations—and how Apache Druid is the ideal foundation for any in-house security solution.

Learn More
May 05, 2023

Introducing Apache Druid 25.0

Apache Druid 25.0 contains over 293 updates from over 56 contributors.

Learn More
May 03, 2023

Druid and SQL syntax

This is a technical blog, which summarises the process of extending the Druid's SQL grammar for ingestion and delves into the nitty gritty of Calcite.

Learn More
May 02, 2023

Native support for semi-structured data in Apache Druid

Describes a new feature- ingest complex data as is into Druid- massive improvement in developer productivity

Learn More
May 01, 2023

Real-Time Analytics with Imply Polaris: From Setup to Visualization

Imply Polaris offers reduced operational overhead and elastic scaling for efficient real-time analytics that helps you unlock your data's potential.

Learn More
May 01, 2023

Datanami Award

Apache Druid won Datanami's 2022 Readers’ and Editors’ Choice Awards for Reader's Choice "Best Data and AI Product or Technology: Analytics Database".

Learn More
Apr 30, 2023

Alerting and Security Features in Polaris

Describes new features - alerts and some security features- and how Imply customers can leverage it

Learn More
Apr 29, 2023

Ingestion from Amazon Kinesis and S3 into Imply Polaris

Imply Polaris now supports data ingestion from Amazon Kinesis and Amazon S3

Learn More
Apr 27, 2023

Getting the Most Out of your Data

Ingesting data from one table to another is easy and fast in Imply Polaris!

Learn More
Apr 26, 2023

Combating financial fraud and money laundering at scale with Apache Druid

Learn how Apache Druid enables financial services firms and FinTech companies to get immediate insights from petabytes-plus data volumes for anti-fraud and anti-money laundering compliance.

Learn More
Apr 26, 2023

What’s new in Imply – December 2022

This is a what's new to Imply in Dec 2022. We’ve added two new features to Imply Polaris to make it easier for your end users to take advantage of real-time insights.

Learn More
Apr 25, 2023

What’s New in Imply Polaris – November 2022

This blog provides an overview for the new features, functionality, and connectivity to Imply Polaris for November 2022.

Learn More
Apr 24, 2023

Imply Pivot delivers the final mile for modern analytics applications

This blog is focused on how Imply Pivot delivers the final mile for building an anlaytics app. It showcases two customer examples - Twitch and ironsource.

Learn More
Apr 23, 2023

Why Analytics Need More than a Data Warehouse

For decades, analytics has been defined by the standard reporting and BI workflow, supported by the data warehouse. Now, 1000s of companies are realizing an expansion of analytics beyond reporting, which requires...

Learn More
Apr 21, 2023

Why Open Source Matters for Databases

Apache Druid is at the heart of Imply. We’re an open source business, and that’s why we’re committed to making Druid the best open source database for modern analytics applications

Learn More
Apr 20, 2023

Ingestion from Confluent Cloud and Kafka in Polaris

How to ingest data into Imply Polaris from Confluent Cloud and from Apache Kafka

Learn More
Apr 18, 2023

What Makes a Database Built for Streaming Data?

For an analytics app to handle real-time, streaming sources, it must be built for streaming data. Druid has 3 essential features for stream data.

Learn More
Oct 12, 2022

SQL-based Transformations and JSON Columns in Imply Polaris

You can easily do data transformations and manage JSON data with Imply Polaris, both using SQL.

Learn More
Oct 06, 2022

Approximate Distinct Counts in Imply Polaris

When it comes to modern data analytics applications, speed is of the utmost importance. In this blog we discuss two approximation algorithms which can be used to greatly enhance speed with only a slight reduction...

Learn More
Sep 20, 2022

The next chapter for Imply Polaris: celebrating 250+ accounts, continued innovation

Today we announced the next iteration of Imply Polaris, the fully managed Database-as-a-Service that helps you build modern analytics applications faster, cheaper, and with less effort. Since its launch in...

Learn More
Sep 20, 2022

Introducing Imply’s Total Value Guarantee for Apache Druid

Apache Druid 24.0 contains 450 updates and new features, major performance enhancements, bug fixes, and major documentation improvements

Learn More
Sep 16, 2022

Introducing Apache Druid 24.0

Apache Druid 24.0 contains 450 updates and new features, major performance enhancements, bug fixes, and major documentation improvements

Learn More
Aug 16, 2022

Using Imply Pivot with Druid to Deduplicate Timeseries Data

Imply Pivot offers multi step aggregations, which is valuable for timeseries data where measures are not evenly distributed in time.

Learn More
Jul 21, 2022

A Look Under the Surface at Polaris Security

We have taken a security-first approach in building the easiest real-time database for modern analytics applications.

Learn More
Jul 14, 2022

Upserts and Data Deduplication with Druid

A look at what can be done with Druid for upserts and data deduplication.

Learn More
Jul 01, 2022

What Developers Can Build with Apache Druid

We obviously talk a lot about #ApacheDruid on here. But what are folks actually building with Druid? What is a modern analytics application, exactly? Let's find out

Learn More
Jun 29, 2022

When Streaming Analytics… Isn’t

Nearly all databases are designed for batch processing, which leaves three options for stream analytics.

Learn More
Jun 29, 2022

Apache Druid vs. Snowflake

Elasticity is important, but beware the database that can only save you money when your application is not in use. The best solution will have excellent price-performance under all conditions.

Learn More
Jun 22, 2022

Druid 0.23 – Features And Capabilities For Advanced Scenarios

Many of Druid’s improvements focus on building a solid foundation, including making the system more stable, easier to use, faster to scale, and better integrated with the rest of the data ecosystem. But for...

Learn More
Jun 22, 2022

Introducing Apache Druid 0.23

Apache Druid 0.23.0 contains over 450 updates, including new features, major performance enhancements, bug fixes, and major documentation improvements.

Learn More
Jun 20, 2022

An Opinionated Guide to Component APIs

We have collected a number of guidelines for React component APIs that make components more predictable in terms of behavior and performance.

Learn More
Jun 10, 2022

Druid Architecture & Concepts

In a world full of databases, learn how Apache Druid makes real-time analytics apps a reality in this Whitepaper from Imply

Learn More
May 25, 2022

3 decisions that shaped the Polaris UI

Imply Polaris is a fully managed database-as-a-service for building realtime analytics applications. John is the tech lead for the Polaris UI, known internally as the Unified App. It began with a profound question:...

Learn More
May 19, 2022

How Imply Polaris takes a security-first approach

A primer for developers on security tools and controls available in Imply Polaris

Learn More
May 17, 2022

Imply Raises $100MM in Series D funding

There is a new category within data analytics emerging which is not centered in the world of reports and dashboards (the purview of data analysts and data scientists), but instead centered in the world of applications...

Learn More
May 11, 2022

Imply Named “Cool Database Vendor” by CRN

There can’t be one database good at everything. When it comes to real-time analytics, you need a database built for it.

Learn More
May 11, 2022

Living the Stream

We are in the early stages of a stream revolution, as developers build modern transactional and analytic applications that use real-time data continuously delivered.

Learn More
May 02, 2022

Migrating Data from ClickHouse to Imply Polaris

In this blog, we’ll review the simple steps to export data from ClickHouse in a format that is easy to ingest into Polaris.

Learn More
Apr 06, 2022

Java Keytool, TLS, and Zookeeper Security

Lean the basics of Public Key Infrastructure (PKI) as it relates to Druid and Zookeeper security.

Learn More
Apr 01, 2022

Building high performance logging analytics with Polaris and Logstash

When you think of querying with Apache Druid, you probably imagine queries over massive data sets that run in less than a second. This blog is about some of the things we did as a team to discover the user...

Learn More
Apr 01, 2022

For April 1st: a New Description of Apache Druid from Our Youngest Technical Architect

A simple set of instructions to deploy Apache Druid on minikube using minio for local deep storage on your laptop.

Learn More

Let us help with your analytics apps

Request a Demo