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.

Summary

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.

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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...

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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

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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.

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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.

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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...

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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...

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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...

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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...

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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...

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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!)

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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.

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May 05, 2023

Introducing Apache Druid 25.0

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

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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.

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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

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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.

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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".

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Apr 30, 2023

Alerting and Security Features in Polaris

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

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Apr 29, 2023

Ingestion from Amazon Kinesis and S3 into Imply Polaris

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

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Apr 27, 2023

Getting the Most Out of your Data

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

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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.

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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.

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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.

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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.

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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...

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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

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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

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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.

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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.

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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...

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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...

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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

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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

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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.

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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.

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Jul 14, 2022

Upserts and Data Deduplication with Druid

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

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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

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Jun 29, 2022

When Streaming Analytics… Isn’t

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

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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.

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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...

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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.

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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.

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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

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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:...

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May 19, 2022

How Imply Polaris takes a security-first approach

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

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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...

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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.

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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.

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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.

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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.

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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...

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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.

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