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REAL-TIME RECOMMENDATIONS FOR EVENT TICKETING USING Real-Time Recommendations for Event Ticketing Using MongoDB and Rockset. When building data-driven applications, it’s been a common practice for years to move analytics away from the source database into either a slave, data warehouse or something similar. The main reason for this is that analytical queries, such as aggregations andjoins
DYNAMIC TYPING IN SQL APICLIENT - ROCKSET DOCUMENTATION selectHeaderContentType. Select the Content-Type header's value from the given array: if JSON exists in the given array, use it; otherwise use the first one of the array. The Content-Type header to use. If the given array is empty, or matches "any", JSON will be used. 5 USE CASES FOR DYNAMODBTABLEAU INTEGRATION
Place the Rockset Java SDK jar in the following folder depending on the operating system: Windows: C:\Program Files\Tableau\Drivers. Mac: ~/Library/Tableau/Drivers. Refer to Tableau Documentation for more detailed instructions. In Tableau, navigate to Connect To a Server and select Other Databases (JDBC): Configure the connection as follows: ROCKSET: THE REAL-TIME INDEXING DATABASE IN THE CLOUDPRICINGCUSTOMERSCONTACT USPRODUCT OVERVIEWARCHITECTURESECURITY Rockset enables interactive real-time analytics in your application - logistics tracking, security analytics, gaming leaderboards and more. Our radical approach makes real-time analytics fast, flexible and easy by indexing every field in your structured, semi-structured, geo or time series data. We’ve removed many of the barriers developers REAL-TIME ANALYTICS ON IOT DATA FROM APACHE KAFKA Discover how to build a real-time analytics stack based on Kafka and Rockset. Step 6. Building the Live IoT Analytics Dashboard with Redash. Redash offers a hosted solution which offers easy integration with Rockset. With a couple of clicks, you can create charts and dashboards, which auto-refresh as new data arrives. REAL-TIME ANALYTICS ON DYNAMODB HOW WE USE ROCKSDB AT ROCKSET At Rockset, we want our users to be able to continuously ingest their data into Rockset with sub-second write latency and query it in 10s of milliseconds. For this, we need a storage engine that can support both fast online writes and fast reads. RocksDB is a high-performance storage engine that is built to support such workloads. ROCKSET VS. APACHE DRUID Rockset vs. Apache Druid. Rockset is pure magic. We chose Rockset over Druid, because it requires no planning whatsoever in terms of indexes or scaling. In one hour, we were up and running, serving complex OLAP queries for our live leaderboards and dashboards at very high queriesper second.
REAL-TIME RECOMMENDATIONS FOR EVENT TICKETING USING Real-Time Recommendations for Event Ticketing Using MongoDB and Rockset. When building data-driven applications, it’s been a common practice for years to move analytics away from the source database into either a slave, data warehouse or something similar. The main reason for this is that analytical queries, such as aggregations andjoins
DYNAMIC TYPING IN SQL APICLIENT - ROCKSET DOCUMENTATION selectHeaderContentType. Select the Content-Type header's value from the given array: if JSON exists in the given array, use it; otherwise use the first one of the array. The Content-Type header to use. If the given array is empty, or matches "any", JSON will be used. 5 USE CASES FOR DYNAMODBTABLEAU INTEGRATION
Place the Rockset Java SDK jar in the following folder depending on the operating system: Windows: C:\Program Files\Tableau\Drivers. Mac: ~/Library/Tableau/Drivers. Refer to Tableau Documentation for more detailed instructions. In Tableau, navigate to Connect To a Server and select Other Databases (JDBC): Configure the connection as follows:COMPANY | ROCKSET
We're a fast-growing company. We value curiosity, diversity and open-mindedness. Rockset is a fun place to work. You will solve interesting problems, surrounded by exceptional people, while making customers happy. We work hard, but also take our personal lives and BUILDING A SERVERLESS MICROSERVICE USING ROCKSET AND AWS Rockset makes it easy to develop serverless microservices, data APIs, and data-driven applications. This video demo shows an example of what's possible with Rockset. For this exercise, we will build a serverless microservice to discover the stock symbols with the most mentions on Twitter. DESIGNING A REAL-TIME ETA PREDICTION SYSTEM USING KAFKA Query service performs the following functions: Fetch the latest smoothing factors Alpha and Beta from DynamoDB. Here, Alpha is the smoothing parameter and Beta is the weight assigned to historical ETA while calculating the final ETA. Refer step 6 for more details. Fetch the destination geohash for the order id. USING TABLEAU WITH KAFKA: HOW TO BUILD A REAL-TIME SQL We used Rockset as a data sink for Kafka event data, in order to provide low-latency SQL to serve real-time Tableau dashboards. The steps we followed were: Start with data in a Kafka topic. Create a collection in Rockset, using the Kafka topic as a source. Write one or more SQL queries that return the data needed in Tableau.STRING FUNCTIONS
Learn about string functions in Rockset's SQL dialect. SPLIT SPLIT(string, delimiter) Splits string on delimiter and returns an array. With limit, only the first limit - 1 delimiters are split upon, thereby returning an array of size at most limit.The last element in the array always contains everything left in the string in the case where there are >= limit occurrences of the STORAGE ARCHITECTURE Storage Architecture. Rockset uses RocksDB, an open source key-value store, to store your data.RocksDB is widely used in many storage systems that require high performance and low latency access. It has become the storage engine of choice for many database management systems, including MySQL, Apache Kafka and CockroachDB. USING MONGODB CHANGE STREAMS FOR INDEXING WITH Users get the added benefit of improved query performance when their queries can make use of the indexing of the second database. Elasticsearch is a common choice for indexing MongoDB data, and users can use change streams to effect a real-time sync from MongoDB to Elasticsearch. Rockset, a real-time indexing database in the cloud, isanother
HANDLING SLOW QUERIES IN MONGODB Handling Slow Queries in MongoDB - Part 2: Solutions. In Part One, we discussed how to first identify slow queries on MongoDB using the database profiler, and then investigated what the strategies the database took doing during the execution of those queries to understand why our queries were taking the time and resources thatthey were taking.
ARRAY FUNCTIONS
#ARRAY_POSITION ARRAY_POSITION(array, val) Return a 1-based index of the first occurrence of val if it is found within array.If val is null, it will look for occurrence of null in the array. If val does not exist within array, it returns 0.TABLEAU INTEGRATION
Place the Rockset Java SDK jar in the following folder depending on the operating system: Windows: C:\Program Files\Tableau\Drivers. Mac: ~/Library/Tableau/Drivers. Refer to Tableau Documentation for more detailed instructions. In Tableau, navigate to Connect To a Server and select Other Databases (JDBC): Configure the connection as follows: ROCKSET: THE REAL-TIME INDEXING DATABASE IN THE CLOUDPRICINGCUSTOMERSCONTACT USPRODUCT OVERVIEWARCHITECTURESECURITY Rockset enables interactive real-time analytics in your application - logistics tracking, security analytics, gaming leaderboards and more. Our radical approach makes real-time analytics fast, flexible and easy by indexing every field in your structured, semi-structured, geo or time series data. We’ve removed many of the barriers developers REAL-TIME ANALYTICS ON IOT DATA FROM APACHE KAFKA Discover how to build a real-time analytics stack based on Kafka and Rockset. Step 6. Building the Live IoT Analytics Dashboard with Redash. Redash offers a hosted solution which offers easy integration with Rockset. With a couple of clicks, you can create charts and dashboards, which auto-refresh as new data arrives. REAL-TIME ANALYTICS ON DYNAMODB CHANGE DATA CAPTURE: WHAT IT IS AND HOW TO USE IT Change data capture (CDC) is a useful tool in many data architectures. Learn what CDC is, how it is implemented and when to use it. REAL-TIME RECOMMENDATIONS FOR EVENT TICKETING USING Real-Time Recommendations for Event Ticketing Using MongoDB and Rockset. When building data-driven applications, it’s been a common practice for years to move analytics away from the source database into either a slave, data warehouse or something similar. The main reason for this is that analytical queries, such as aggregations andjoins
HELLO ROCKSET
The opportunity to work on a cloud-native solution is compelling as it provides new ways to make things better, especially when using an LSM. Two examples are RocksDB Cloud and disaggregating LSM compaction. RocksDB Cloud adapts RocksDB to use object storage like S3. The high durability of cloud object storage lets me focus on solving higherSTRING FUNCTIONS
Learn about string functions in Rockset's SQL dialect. SPLIT SPLIT(string, delimiter) Splits string on delimiter and returns an array. With limit, only the first limit - 1 delimiters are split upon, thereby returning an array of size at most limit.The last element in the array always contains everything left in the string in the case where there are >= limit occurrences of the ANALYTICS ON DYNAMODB: COMPARING ELASTICSEARCH, ATHENA Analytics on DynamoDB: Comparing Elasticsearch, Athena, and Spark. In this blog I compare options for real-time analytics on DynamoDB - Elasticsearch, Athena, and Spark - in terms of ease of setup, maintenance, query capability, latency. There is limited support for SQL analytics with some of these options. I also evaluate which usecases each
5 USE CASES FOR DYNAMODB USING TABLEAU FOR LIVE DASHBOARDS ON EVENT DATA ROCKSET: THE REAL-TIME INDEXING DATABASE IN THE CLOUDPRICINGCUSTOMERSCONTACT USPRODUCT OVERVIEWARCHITECTURESECURITY Rockset enables interactive real-time analytics in your application - logistics tracking, security analytics, gaming leaderboards and more. Our radical approach makes real-time analytics fast, flexible and easy by indexing every field in your structured, semi-structured, geo or time series data. We’ve removed many of the barriers developers REAL-TIME ANALYTICS ON IOT DATA FROM APACHE KAFKA Discover how to build a real-time analytics stack based on Kafka and Rockset. Step 6. Building the Live IoT Analytics Dashboard with Redash. Redash offers a hosted solution which offers easy integration with Rockset. With a couple of clicks, you can create charts and dashboards, which auto-refresh as new data arrives. REAL-TIME ANALYTICS ON DYNAMODB CHANGE DATA CAPTURE: WHAT IT IS AND HOW TO USE IT Change data capture (CDC) is a useful tool in many data architectures. Learn what CDC is, how it is implemented and when to use it. REAL-TIME RECOMMENDATIONS FOR EVENT TICKETING USING Real-Time Recommendations for Event Ticketing Using MongoDB and Rockset. When building data-driven applications, it’s been a common practice for years to move analytics away from the source database into either a slave, data warehouse or something similar. The main reason for this is that analytical queries, such as aggregations andjoins
HELLO ROCKSET
The opportunity to work on a cloud-native solution is compelling as it provides new ways to make things better, especially when using an LSM. Two examples are RocksDB Cloud and disaggregating LSM compaction. RocksDB Cloud adapts RocksDB to use object storage like S3. The high durability of cloud object storage lets me focus on solving higherSTRING FUNCTIONS
Learn about string functions in Rockset's SQL dialect. SPLIT SPLIT(string, delimiter) Splits string on delimiter and returns an array. With limit, only the first limit - 1 delimiters are split upon, thereby returning an array of size at most limit.The last element in the array always contains everything left in the string in the case where there are >= limit occurrences of the ANALYTICS ON DYNAMODB: COMPARING ELASTICSEARCH, ATHENA Analytics on DynamoDB: Comparing Elasticsearch, Athena, and Spark. In this blog I compare options for real-time analytics on DynamoDB - Elasticsearch, Athena, and Spark - in terms of ease of setup, maintenance, query capability, latency. There is limited support for SQL analytics with some of these options. I also evaluate which usecases each
5 USE CASES FOR DYNAMODB USING TABLEAU FOR LIVE DASHBOARDS ON EVENT DATACOMPANY | ROCKSET
We're a fast-growing company. We value curiosity, diversity and open-mindedness. Rockset is a fun place to work. You will solve interesting problems, surrounded by exceptional people, while making customers happy. We work hard, but also take our personal lives andOUR CUSTOMERS
What Our Customers Are Saying. " Rockset fits all the requirements that we have for a new kind of database. It's serverless, real-time, provides a common API like SQL, and is able to ingest event data easily via a Kafka connector. It's also blazingly fast as compared to on-prem databases we have used in the past, making it a great fit for ROCKSET DOCUMENTATION Rockset Documentation. Rockset is a real-time analytics solution enabling low latency search, aggregations, and joins on massive semi-structured data, without operational burden. Rockset automatically indexes your data – structured, semi-structured, geo and time series data – for real-time search and analytics at scale. BUILDING A SERVERLESS MICROSERVICE USING ROCKSET AND AWS Rockset allows you to export your SQL query and embed it as is into your code. For our demo, we've built a Python -based serverless API, using AWS Lambda, that returns the stock symbols occurring most often in tweets. (Other language clients, including Node.js, Go, and Java, are also available.) Once set up, we can serve live queries on rawHELLO ROCKSET
The opportunity to work on a cloud-native solution is compelling as it provides new ways to make things better, especially when using an LSM. Two examples are RocksDB Cloud and disaggregating LSM compaction. RocksDB Cloud adapts RocksDB to use object storage like S3. The high durability of cloud object storage lets me focus on solving higher OPERATIONAL ANALYTICS: BUILDING LOW-LATENCY QUERIES A time-series database is a specialized operational analytics database. Queries are low latency and it can support high concurrency of queries. Examples of time-series databases are Druid, InfluxDB and TimescaleDB. It can support a complex aggregations on one dimension and that dimension is ‘time’. On the other hand, an OPAP systemcan
REAL-TIME RECOMMENDATIONS FOR EVENT TICKETING USING Real-Time Recommendations for Event Ticketing Using MongoDB and Rockset. When building data-driven applications, it’s been a common practice for years to move analytics away from the source database into either a slave, data warehouse or something similar. The main reason for this is that analytical queries, such as aggregations andjoins
USING TABLEAU WITH KAFKA: HOW TO BUILD A REAL-TIME SQL We used Rockset as a data sink for Kafka event data, in order to provide low-latency SQL to serve real-time Tableau dashboards. The steps we followed were: Start with data in a Kafka topic. Create a collection in Rockset, using the Kafka topic as a source. Write one or more SQL queries that return the data needed in Tableau. APICLIENT - ROCKSET DOCUMENTATION selectHeaderContentType. Select the Content-Type header's value from the given array: if JSON exists in the given array, use it; otherwise use the first one of the array. The Content-Type header to use. If the given array is empty, or matches "any", JSON will be used. WHY SQL ON RAW DATA? Querying an unstructured data source using SQL for use in analytics, data science, and application development requires a sequence of tedious steps: figure out how the data is currently formatted, determine a desired schema, input this schema into a SQL engine, and finally load the data and issue queries. This setup is a majoroverhead, and
ROCKSET: THE REAL-TIME INDEXING DATABASE IN THE CLOUDPRICINGCUSTOMERSCONTACT USPRODUCT OVERVIEWARCHITECTURESECURITY Rockset enables interactive real-time analytics in your application - logistics tracking, security analytics, gaming leaderboards and more. Our radical approach makes real-time analytics fast, flexible and easy by indexing every field in your structured, semi-structured, geo or time series data. We’ve removed many of the barriers developers REAL-TIME ANALYTICS ON IOT DATA FROM APACHE KAFKA Discover how to build a real-time analytics stack based on Kafka and Rockset. Step 6. Building the Live IoT Analytics Dashboard with Redash. Redash offers a hosted solution which offers easy integration with Rockset. With a couple of clicks, you can create charts and dashboards, which auto-refresh as new data arrives. REAL-TIME ANALYTICS ON DYNAMODB DESIGNING A REAL-TIME ETA PREDICTION SYSTEM USING KAFKASEE MORE ONROCKSET.COM
REAL-TIME RECOMMENDATIONS FOR EVENT TICKETING USING Real-Time Recommendations for Event Ticketing Using MongoDB and Rockset. When building data-driven applications, it’s been a common practice for years to move analytics away from the source database into either a slave, data warehouse or something similar. The main reason for this is that analytical queries, such as aggregations andjoins
HOW WE USE ROCKSDB AT ROCKSET At Rockset, we want our users to be able to continuously ingest their data into Rockset with sub-second write latency and query it in 10s of milliseconds. For this, we need a storage engine that can support both fast online writes and fast reads. RocksDB is a high-performance storage engine that is built to support such workloads. ROCKSET VS. APACHE DRUID Rockset vs. Apache Druid. Rockset is pure magic. We chose Rockset over Druid, because it requires no planning whatsoever in terms of indexes or scaling. In one hour, we were up and running, serving complex OLAP queries for our live leaderboards and dashboards at very high queriesper second.
5 USE CASES FOR DYNAMODB APICLIENT - ROCKSET DOCUMENTATION selectHeaderContentType. Select the Content-Type header's value from the given array: if JSON exists in the given array, use it; otherwise use the first one of the array. The Content-Type header to use. If the given array is empty, or matches "any", JSON will be used.TABLEAU INTEGRATION
Place the Rockset Java SDK jar in the following folder depending on the operating system: Windows: C:\Program Files\Tableau\Drivers. Mac: ~/Library/Tableau/Drivers. Refer to Tableau Documentation for more detailed instructions. In Tableau, navigate to Connect To a Server and select Other Databases (JDBC): Configure the connection as follows: ROCKSET: THE REAL-TIME INDEXING DATABASE IN THE CLOUDPRICINGCUSTOMERSCONTACT USPRODUCT OVERVIEWARCHITECTURESECURITY Rockset enables interactive real-time analytics in your application - logistics tracking, security analytics, gaming leaderboards and more. Our radical approach makes real-time analytics fast, flexible and easy by indexing every field in your structured, semi-structured, geo or time series data. We’ve removed many of the barriers developers REAL-TIME ANALYTICS ON IOT DATA FROM APACHE KAFKA Discover how to build a real-time analytics stack based on Kafka and Rockset. Step 6. Building the Live IoT Analytics Dashboard with Redash. Redash offers a hosted solution which offers easy integration with Rockset. With a couple of clicks, you can create charts and dashboards, which auto-refresh as new data arrives. REAL-TIME ANALYTICS ON DYNAMODB DESIGNING A REAL-TIME ETA PREDICTION SYSTEM USING KAFKASEE MORE ONROCKSET.COM
REAL-TIME RECOMMENDATIONS FOR EVENT TICKETING USING Real-Time Recommendations for Event Ticketing Using MongoDB and Rockset. When building data-driven applications, it’s been a common practice for years to move analytics away from the source database into either a slave, data warehouse or something similar. The main reason for this is that analytical queries, such as aggregations andjoins
HOW WE USE ROCKSDB AT ROCKSET At Rockset, we want our users to be able to continuously ingest their data into Rockset with sub-second write latency and query it in 10s of milliseconds. For this, we need a storage engine that can support both fast online writes and fast reads. RocksDB is a high-performance storage engine that is built to support such workloads. ROCKSET VS. APACHE DRUID Rockset vs. Apache Druid. Rockset is pure magic. We chose Rockset over Druid, because it requires no planning whatsoever in terms of indexes or scaling. In one hour, we were up and running, serving complex OLAP queries for our live leaderboards and dashboards at very high queriesper second.
5 USE CASES FOR DYNAMODB APICLIENT - ROCKSET DOCUMENTATION selectHeaderContentType. Select the Content-Type header's value from the given array: if JSON exists in the given array, use it; otherwise use the first one of the array. The Content-Type header to use. If the given array is empty, or matches "any", JSON will be used.TABLEAU INTEGRATION
Place the Rockset Java SDK jar in the following folder depending on the operating system: Windows: C:\Program Files\Tableau\Drivers. Mac: ~/Library/Tableau/Drivers. Refer to Tableau Documentation for more detailed instructions. In Tableau, navigate to Connect To a Server and select Other Databases (JDBC): Configure the connection as follows:COMPANY | ROCKSET
We're a fast-growing company. We value curiosity, diversity and open-mindedness. Rockset is a fun place to work. You will solve interesting problems, surrounded by exceptional people, while making customers happy. We work hard, but also take our personal lives andPRICING | ROCKSET
In September, you build a geosearch feature for your IoT device using Kafka and Rockset. You ingest 10GB of uncompressed data daily. The total size of data on disk after compression and indexing for every day is 20GB. You retain the data for 1 day. You select a Dedicated Virtual Instance at $0.7989/hr to run compute heavy queries. BUILDING A SERVERLESS MICROSERVICE USING ROCKSET AND AWS Rockset makes it easy to develop serverless microservices, data APIs, and data-driven applications. This video demo shows an example of what's possible with Rockset. For this exercise, we will build a serverless microservice to discover the stock symbols with the most mentions on Twitter. CHANGE DATA CAPTURE: WHAT IT IS AND HOW TO USE IT Change data capture (CDC) is a useful tool in many data architectures. Learn what CDC is, how it is implemented and when to use it. DESIGNING A REAL-TIME ETA PREDICTION SYSTEM USING KAFKA Query service performs the following functions: Fetch the latest smoothing factors Alpha and Beta from DynamoDB. Here, Alpha is the smoothing parameter and Beta is the weight assigned to historical ETA while calculating the final ETA. Refer step 6 for more details. Fetch the destination geohash for the order id. USING TABLEAU WITH KAFKA: HOW TO BUILD A REAL-TIME SQL We used Rockset as a data sink for Kafka event data, in order to provide low-latency SQL to serve real-time Tableau dashboards. The steps we followed were: Start with data in a Kafka topic. Create a collection in Rockset, using the Kafka topic as a source. Write one or more SQL queries that return the data needed in Tableau.STRING FUNCTIONS
Learn about string functions in Rockset's SQL dialect. SPLIT SPLIT(string, delimiter) Splits string on delimiter and returns an array. With limit, only the first limit - 1 delimiters are split upon, thereby returning an array of size at most limit.The last element in the array always contains everything left in the string in the case where there are >= limit occurrences of the STORAGE ARCHITECTURE Storage Architecture. Rockset uses RocksDB, an open source key-value store, to store your data.RocksDB is widely used in many storage systems that require high performance and low latency access. It has become the storage engine of choice for many database management systems, including MySQL, Apache Kafka and CockroachDB.ARRAY FUNCTIONS
#ARRAY_POSITION ARRAY_POSITION(array, val) Return a 1-based index of the first occurrence of val if it is found within array.If val is null, it will look for occurrence of null in the array. If val does not exist within array, it returns 0.API REFERENCE
The Rockset API allows you to programmatically access and manage your Rockset resources and features. It follows REST architectural principles, and speaks exclusively in JSON. The base URL of the Rockset API server is: https://api.rs2.usw2.rockset.com. All endpoints are accessible via HTTPS only, ensuring that all data in flight isfully
ROCKSET: THE REAL-TIME INDEXING DATABASE IN THE CLOUDPRICINGCUSTOMERSCONTACT USPRODUCT OVERVIEWARCHITECTURESECURITY Rockset enables interactive real-time analytics in your application - logistics tracking, security analytics, gaming leaderboards and more. Our radical approach makes real-time analytics fast, flexible and easy by indexing every field in your structured, semi-structured, geo or time series data. We’ve removed many of the barriers developers ROCKSET DOCUMENTATION Rockset Documentation. Rockset is a real-time analytics solution enabling low latency search, aggregations, and joins on massive semi-structured data, without operational burden. Rockset automatically indexes your data – structured, semi-structured, geo and time series data – for real-time search and analytics at scale. REAL-TIME ANALYTICS ON IOT DATA FROM APACHE KAFKA Discover how to build a real-time analytics stack based on Kafka and Rockset. Step 6. Building the Live IoT Analytics Dashboard with Redash. Redash offers a hosted solution which offers easy integration with Rockset. With a couple of clicks, you can create charts and dashboards, which auto-refresh as new data arrives. REAL-TIME ANALYTICS ON DYNAMODB ROCKSET VS. APACHE DRUID Rockset vs. Apache Druid. Rockset is pure magic. We chose Rockset over Druid, because it requires no planning whatsoever in terms of indexes or scaling. In one hour, we were up and running, serving complex OLAP queries for our live leaderboards and dashboards at very high queriesper second.
REAL-TIME RECOMMENDATIONS FOR EVENT TICKETING USING Real-Time Recommendations for Event Ticketing Using MongoDB and Rockset. When building data-driven applications, it’s been a common practice for years to move analytics away from the source database into either a slave, data warehouse or something similar. The main reason for this is that analytical queries, such as aggregations andjoins
ARRAY FUNCTIONS
#ARRAY_POSITION ARRAY_POSITION(array, val) Return a 1-based index of the first occurrence of val if it is found within array.If val is null, it will look for occurrence of null in the array. If val does not exist within array, it returns 0. 5 USE CASES FOR DYNAMODB APICLIENT - ROCKSET DOCUMENTATION selectHeaderContentType. Select the Content-Type header's value from the given array: if JSON exists in the given array, use it; otherwise use the first one of the array. The Content-Type header to use. If the given array is empty, or matches "any", JSON will be used.TABLEAU INTEGRATION
Place the Rockset Java SDK jar in the following folder depending on the operating system: Windows: C:\Program Files\Tableau\Drivers. Mac: ~/Library/Tableau/Drivers. Refer to Tableau Documentation for more detailed instructions. In Tableau, navigate to Connect To a Server and select Other Databases (JDBC): Configure the connection as follows: ROCKSET: THE REAL-TIME INDEXING DATABASE IN THE CLOUDPRICINGCUSTOMERSCONTACT USPRODUCT OVERVIEWARCHITECTURESECURITY Rockset enables interactive real-time analytics in your application - logistics tracking, security analytics, gaming leaderboards and more. Our radical approach makes real-time analytics fast, flexible and easy by indexing every field in your structured, semi-structured, geo or time series data. We’ve removed many of the barriers developers ROCKSET DOCUMENTATION Rockset Documentation. Rockset is a real-time analytics solution enabling low latency search, aggregations, and joins on massive semi-structured data, without operational burden. Rockset automatically indexes your data – structured, semi-structured, geo and time series data – for real-time search and analytics at scale. REAL-TIME ANALYTICS ON IOT DATA FROM APACHE KAFKA Discover how to build a real-time analytics stack based on Kafka and Rockset. Step 6. Building the Live IoT Analytics Dashboard with Redash. Redash offers a hosted solution which offers easy integration with Rockset. With a couple of clicks, you can create charts and dashboards, which auto-refresh as new data arrives. REAL-TIME ANALYTICS ON DYNAMODB ROCKSET VS. APACHE DRUID Rockset vs. Apache Druid. Rockset is pure magic. We chose Rockset over Druid, because it requires no planning whatsoever in terms of indexes or scaling. In one hour, we were up and running, serving complex OLAP queries for our live leaderboards and dashboards at very high queriesper second.
REAL-TIME RECOMMENDATIONS FOR EVENT TICKETING USING Real-Time Recommendations for Event Ticketing Using MongoDB and Rockset. When building data-driven applications, it’s been a common practice for years to move analytics away from the source database into either a slave, data warehouse or something similar. The main reason for this is that analytical queries, such as aggregations andjoins
ARRAY FUNCTIONS
#ARRAY_POSITION ARRAY_POSITION(array, val) Return a 1-based index of the first occurrence of val if it is found within array.If val is null, it will look for occurrence of null in the array. If val does not exist within array, it returns 0. 5 USE CASES FOR DYNAMODB APICLIENT - ROCKSET DOCUMENTATION selectHeaderContentType. Select the Content-Type header's value from the given array: if JSON exists in the given array, use it; otherwise use the first one of the array. The Content-Type header to use. If the given array is empty, or matches "any", JSON will be used.TABLEAU INTEGRATION
Place the Rockset Java SDK jar in the following folder depending on the operating system: Windows: C:\Program Files\Tableau\Drivers. Mac: ~/Library/Tableau/Drivers. Refer to Tableau Documentation for more detailed instructions. In Tableau, navigate to Connect To a Server and select Other Databases (JDBC): Configure the connection as follows: USING TABLEAU WITH KAFKA: HOW TO BUILD A REAL-TIME SQL We used Rockset as a data sink for Kafka event data, in order to provide low-latency SQL to serve real-time Tableau dashboards. The steps we followed were: Start with data in a Kafka topic. Create a collection in Rockset, using the Kafka topic as a source. Write one or more SQL queries that return the data needed in Tableau. HOW WE USE ROCKSDB AT ROCKSET At Rockset, we want our users to be able to continuously ingest their data into Rockset with sub-second write latency and query it in 10s of milliseconds. For this, we need a storage engine that can support both fast online writes and fast reads. RocksDB is a high-performance storage engine that is built to support such workloads. DYNAMIC TYPING IN SQL Dynamic Typing in SQL. As Peter Bailis put it in his post, querying unstructured data using SQL is a painful process. Moreover, developers frequently prefer dynamic programming languages, so interacting with the strict type system of SQL is a barrier. We at Rockset have built the first schemaless SQL data platform. OPTIMIZING BULK LOAD IN ROCKSDB The most common advice for bulk loading data into RocksDB is to turn off compactions and execute one big compaction in the end. This setup is also mentioned in the official RocksDB Performance Benchmarks. After all, the only reason RocksDB executes compactions is to optimize reads at the expense of write overhead. DESIGNING A REAL-TIME ETA PREDICTION SYSTEM USING KAFKA Query service performs the following functions: Fetch the latest smoothing factors Alpha and Beta from DynamoDB. Here, Alpha is the smoothing parameter and Beta is the weight assigned to historical ETA while calculating the final ETA. Refer step 6 for more details. Fetch the destination geohash for the order id. BUILDING A SERVERLESS MICROSERVICE USING ROCKSET AND AWS Rockset makes it easy to develop serverless microservices, data APIs, and data-driven applications. This video demo shows an example of what's possible with Rockset. For this exercise, we will build a serverless microservice to discover the stock symbols with the most mentions on Twitter.ARRAY FUNCTIONS
#ARRAY_POSITION ARRAY_POSITION(array, val) Return a 1-based index of the first occurrence of val if it is found within array.If val is null, it will look for occurrence of null in the array. If val does not exist within array, it returns 0. STORAGE ARCHITECTURE Storage Architecture. Rockset uses RocksDB, an open source key-value store, to store your data.RocksDB is widely used in many storage systems that require high performance and low latency access. It has become the storage engine of choice for many database management systems, including MySQL, Apache Kafka and CockroachDB.STRING FUNCTIONS
Learn about string functions in Rockset's SQL dialect. SPLIT SPLIT(string, delimiter) Splits string on delimiter and returns an array. With limit, only the first limit - 1 delimiters are split upon, thereby returning an array of size at most limit.The last element in the array always contains everything left in the string in the case where there are >= limit occurrences of theAPI REFERENCE
The Rockset API allows you to programmatically access and manage your Rockset resources and features. It follows REST architectural principles, and speaks exclusively in JSON. The base URL of the Rockset API server is: https://api.rs2.usw2.rockset.com. All endpoints are accessible via HTTPS only, ensuring that all data in flight isfully
ROCKSET: THE REAL-TIME INDEXING DATABASE IN THE CLOUDPRICINGCUSTOMERSCONTACT USPRODUCT OVERVIEWARCHITECTURESECURITY Rockset enables interactive real-time analytics in your application - logistics tracking, security analytics, gaming leaderboards and more. Our radical approach makes real-time analytics fast, flexible and easy by indexing every field in your structured, semi-structured, geo or time series data. We’ve removed many of the barriers developers ROCKSET DOCUMENTATION Rockset Documentation. Rockset is a real-time analytics solution enabling low latency search, aggregations, and joins on massive semi-structured data, without operational burden. Rockset automatically indexes your data – structured, semi-structured, geo and time series data – for real-time search and analytics at scale. REAL-TIME ANALYTICS ON IOT DATA FROM APACHE KAFKA Discover how to build a real-time analytics stack based on Kafka and Rockset. Step 6. Building the Live IoT Analytics Dashboard with Redash. Redash offers a hosted solution which offers easy integration with Rockset. With a couple of clicks, you can create charts and dashboards, which auto-refresh as new data arrives. BUILDING A SERVERLESS MICROSERVICE USING ROCKSET AND AWS Rockset allows you to export your SQL query and embed it as is into your code. For our demo, we've built a Python -based serverless API, using AWS Lambda, that returns the stock symbols occurring most often in tweets. (Other language clients, including Node.js, Go, and Java, are also available.) Once set up, we can serve live queries on raw REAL-TIME ANALYTICS ON DYNAMODB WORKSPACES | ROCKSET DOCUMENTATION Workspaces. A workspace is a container that can hold Collections, Collection Aliases, Query Lambdas, and other workspaces.. In this way, workspaces are analogous to folders, whereas their contents (such as collections and Query Lambdas) are analogous to files. DYNAMIC TYPING IN SQL REAL-TIME RECOMMENDATIONS FOR EVENT TICKETING USING Real-Time Recommendations for Event Ticketing Using MongoDB and Rockset. When building data-driven applications, it’s been a common practice for years to move analytics away from the source database into either a slave, data warehouse or something similar. The main reason for this is that analytical queries, such as aggregations andjoins
ARRAY FUNCTIONS
#ARRAY_POSITION ARRAY_POSITION(array, val) Return a 1-based index of the first occurrence of val if it is found within array.If val is null, it will look for occurrence of null in the array. If val does not exist within array, it returns 0. BEST PRACTICES FOR ANALYZING KAFKA EVENT STREAMS ROCKSET: THE REAL-TIME INDEXING DATABASE IN THE CLOUDPRICINGCUSTOMERSCONTACT USPRODUCT OVERVIEWARCHITECTURESECURITY Rockset enables interactive real-time analytics in your application - logistics tracking, security analytics, gaming leaderboards and more. Our radical approach makes real-time analytics fast, flexible and easy by indexing every field in your structured, semi-structured, geo or time series data. We’ve removed many of the barriers developers ROCKSET DOCUMENTATION Rockset Documentation. Rockset is a real-time analytics solution enabling low latency search, aggregations, and joins on massive semi-structured data, without operational burden. Rockset automatically indexes your data – structured, semi-structured, geo and time series data – for real-time search and analytics at scale. REAL-TIME ANALYTICS ON IOT DATA FROM APACHE KAFKA Discover how to build a real-time analytics stack based on Kafka and Rockset. Step 6. Building the Live IoT Analytics Dashboard with Redash. Redash offers a hosted solution which offers easy integration with Rockset. With a couple of clicks, you can create charts and dashboards, which auto-refresh as new data arrives. BUILDING A SERVERLESS MICROSERVICE USING ROCKSET AND AWS Rockset allows you to export your SQL query and embed it as is into your code. For our demo, we've built a Python -based serverless API, using AWS Lambda, that returns the stock symbols occurring most often in tweets. (Other language clients, including Node.js, Go, and Java, are also available.) Once set up, we can serve live queries on raw REAL-TIME ANALYTICS ON DYNAMODB WORKSPACES | ROCKSET DOCUMENTATION Workspaces. A workspace is a container that can hold Collections, Collection Aliases, Query Lambdas, and other workspaces.. In this way, workspaces are analogous to folders, whereas their contents (such as collections and Query Lambdas) are analogous to files. DYNAMIC TYPING IN SQL REAL-TIME RECOMMENDATIONS FOR EVENT TICKETING USING Real-Time Recommendations for Event Ticketing Using MongoDB and Rockset. When building data-driven applications, it’s been a common practice for years to move analytics away from the source database into either a slave, data warehouse or something similar. The main reason for this is that analytical queries, such as aggregations andjoins
ARRAY FUNCTIONS
#ARRAY_POSITION ARRAY_POSITION(array, val) Return a 1-based index of the first occurrence of val if it is found within array.If val is null, it will look for occurrence of null in the array. If val does not exist within array, it returns 0. BEST PRACTICES FOR ANALYZING KAFKA EVENT STREAMSCOMPANY | ROCKSET
We're a fast-growing company. We value curiosity, diversity and open-mindedness. Rockset is a fun place to work. You will solve interesting problems, surrounded by exceptional people, while making customers happy. We work hard, but also take our personal lives and BLOG: ARCHITECTURE, HOW-TO GUIDES, CASE STUDIES Case Study: Rumble’s Real-Time Leaderboards Empower Users to Lead Healthier Lifestyles. Rumble encourages people to lead healthy lifestyles by providing incentives based on how much walking they do. Rockset to powers Rumble's real-time leaderboards, which serve to motivate its users to keep active. Nadine Farah. ROCKSET CONVERGED INDEX ADDS CLUSTERED SEARCH INDEX FOR 70 In this blog, we will describe a new storage format that we adopted for our search index, one of the indexes in Rockset’s Converged Index. This new format reduced latencies for common queries by as much as 70% and the size of the search index by about 20%. ELASTICSEARCH OR ROCKSET FOR REAL-TIME ANALYTICS First, Elasticsearch still plays an important role in use cases like text search and log analytics. However, Rockset is better suited to complex real-time search and analytics involving business data. For example, Rockset is a great database for developers building logistics management apps, gaming leaderboards, fraud detection systems, andSTRING FUNCTIONS
Learn about string functions in Rockset's SQL dialect. SPLIT SPLIT(string, delimiter) Splits string on delimiter and returns an array. With limit, only the first limit - 1 delimiters are split upon, thereby returning an array of size at most limit.The last element in the array always contains everything left in the string in the case where there are >= limit occurrences of theHELLO ROCKSET
The opportunity to work on a cloud-native solution is compelling as it provides new ways to make things better, especially when using an LSM. Two examples are RocksDB Cloud and disaggregating LSM compaction. RocksDB Cloud adapts RocksDB to use object storage like S3. The high durability of cloud object storage lets me focus on solving higher OPERATIONAL ANALYTICS: BUILDING LOW-LATENCY QUERIES A time-series database is a specialized operational analytics database. Queries are low latency and it can support high concurrency of queries. Examples of time-series databases are Druid, InfluxDB and TimescaleDB. It can support a complex aggregations on one dimension and that dimension is ‘time’. On the other hand, an OPAP systemcan
BEST PRACTICES FOR ANALYZING KAFKA EVENT STREAMS Conclusion. Building real-time analytics on Kafka event streams involves careful consideration of each of these aspects to ensure the capabilities of the analytics stack meet the requirements of your application and engineering team. Elasticsearch, Druid, Postgres, and Rockset are commonly used as real-time databases to serve analytics ondata
IDENTITY & ACCESS MANAGEMENT Identity & Access Management. This page covers how to manage authentication and authorization in your Rockset organization. # API Keys All requests made to the Rockset API must be authorized with a Rockset API key. API keys can be created in the API Keys tab of the Rockset Console. Once the first API key is created using the Rockset Console, you can use that API key to access the Rockset API APICLIENT - ROCKSET DOCUMENTATION selectHeaderContentType. Select the Content-Type header's value from the given array: if JSON exists in the given array, use it; otherwise use the first one of the array. The Content-Type header to use. If the given array is empty, or matches "any", JSON will be used. ROCKSET: THE REAL-TIME INDEXING DATABASE IN THE CLOUDPRICINGCUSTOMERSCONTACT USPRODUCT OVERVIEWARCHITECTURESECURITY Rockset enables interactive real-time analytics in your application - logistics tracking, security analytics, gaming leaderboards and more. Our radical approach makes real-time analytics fast, flexible and easy by indexing every field in your structured, semi-structured, geo or time series data. We’ve removed many of the barriers developers ROCKSET DOCUMENTATION Rockset Documentation. Rockset is a real-time analytics solution enabling low latency search, aggregations, and joins on massive semi-structured data, without operational burden. Rockset automatically indexes your data – structured, semi-structured, geo and time series data – for real-time search and analytics at scale. REAL-TIME ANALYTICS ON IOT DATA FROM APACHE KAFKA Discover how to build a real-time analytics stack based on Kafka and Rockset. Step 6. Building the Live IoT Analytics Dashboard with Redash. Redash offers a hosted solution which offers easy integration with Rockset. With a couple of clicks, you can create charts and dashboards, which auto-refresh as new data arrives. BUILDING A SERVERLESS MICROSERVICE USING ROCKSET AND AWS Rockset allows you to export your SQL query and embed it as is into your code. For our demo, we've built a Python -based serverless API, using AWS Lambda, that returns the stock symbols occurring most often in tweets. (Other language clients, including Node.js, Go, and Java, are also available.) Once set up, we can serve live queries on raw REAL-TIME ANALYTICS ON DYNAMODB WORKSPACES | ROCKSET DOCUMENTATION Workspaces. A workspace is a container that can hold Collections, Collection Aliases, Query Lambdas, and other workspaces.. In this way, workspaces are analogous to folders, whereas their contents (such as collections and Query Lambdas) are analogous to files. DYNAMIC TYPING IN SQL REAL-TIME RECOMMENDATIONS FOR EVENT TICKETING USING Real-Time Recommendations for Event Ticketing Using MongoDB and Rockset. When building data-driven applications, it’s been a common practice for years to move analytics away from the source database into either a slave, data warehouse or something similar. The main reason for this is that analytical queries, such as aggregations andjoins
ARRAY FUNCTIONS
#ARRAY_POSITION ARRAY_POSITION(array, val) Return a 1-based index of the first occurrence of val if it is found within array.If val is null, it will look for occurrence of null in the array. If val does not exist within array, it returns 0. BEST PRACTICES FOR ANALYZING KAFKA EVENT STREAMS ROCKSET: THE REAL-TIME INDEXING DATABASE IN THE CLOUDPRICINGCUSTOMERSCONTACT USPRODUCT OVERVIEWARCHITECTURESECURITY Rockset enables interactive real-time analytics in your application - logistics tracking, security analytics, gaming leaderboards and more. Our radical approach makes real-time analytics fast, flexible and easy by indexing every field in your structured, semi-structured, geo or time series data. We’ve removed many of the barriers developers ROCKSET DOCUMENTATION Rockset Documentation. Rockset is a real-time analytics solution enabling low latency search, aggregations, and joins on massive semi-structured data, without operational burden. Rockset automatically indexes your data – structured, semi-structured, geo and time series data – for real-time search and analytics at scale. REAL-TIME ANALYTICS ON IOT DATA FROM APACHE KAFKA Discover how to build a real-time analytics stack based on Kafka and Rockset. Step 6. Building the Live IoT Analytics Dashboard with Redash. Redash offers a hosted solution which offers easy integration with Rockset. With a couple of clicks, you can create charts and dashboards, which auto-refresh as new data arrives. BUILDING A SERVERLESS MICROSERVICE USING ROCKSET AND AWS Rockset allows you to export your SQL query and embed it as is into your code. For our demo, we've built a Python -based serverless API, using AWS Lambda, that returns the stock symbols occurring most often in tweets. (Other language clients, including Node.js, Go, and Java, are also available.) Once set up, we can serve live queries on raw REAL-TIME ANALYTICS ON DYNAMODB WORKSPACES | ROCKSET DOCUMENTATION Workspaces. A workspace is a container that can hold Collections, Collection Aliases, Query Lambdas, and other workspaces.. In this way, workspaces are analogous to folders, whereas their contents (such as collections and Query Lambdas) are analogous to files. DYNAMIC TYPING IN SQL REAL-TIME RECOMMENDATIONS FOR EVENT TICKETING USING Real-Time Recommendations for Event Ticketing Using MongoDB and Rockset. When building data-driven applications, it’s been a common practice for years to move analytics away from the source database into either a slave, data warehouse or something similar. The main reason for this is that analytical queries, such as aggregations andjoins
ARRAY FUNCTIONS
#ARRAY_POSITION ARRAY_POSITION(array, val) Return a 1-based index of the first occurrence of val if it is found within array.If val is null, it will look for occurrence of null in the array. If val does not exist within array, it returns 0. BEST PRACTICES FOR ANALYZING KAFKA EVENT STREAMSCOMPANY | ROCKSET
We're a fast-growing company. We value curiosity, diversity and open-mindedness. Rockset is a fun place to work. You will solve interesting problems, surrounded by exceptional people, while making customers happy. We work hard, but also take our personal lives and BLOG: ARCHITECTURE, HOW-TO GUIDES, CASE STUDIES Case Study: Rumble’s Real-Time Leaderboards Empower Users to Lead Healthier Lifestyles. Rumble encourages people to lead healthy lifestyles by providing incentives based on how much walking they do. Rockset to powers Rumble's real-time leaderboards, which serve to motivate its users to keep active. Nadine Farah. ROCKSET CONVERGED INDEX ADDS CLUSTERED SEARCH INDEX FOR 70 In this blog, we will describe a new storage format that we adopted for our search index, one of the indexes in Rockset’s Converged Index. This new format reduced latencies for common queries by as much as 70% and the size of the search index by about 20%. ELASTICSEARCH OR ROCKSET FOR REAL-TIME ANALYTICS First, Elasticsearch still plays an important role in use cases like text search and log analytics. However, Rockset is better suited to complex real-time search and analytics involving business data. For example, Rockset is a great database for developers building logistics management apps, gaming leaderboards, fraud detection systems, andSTRING FUNCTIONS
Learn about string functions in Rockset's SQL dialect. SPLIT SPLIT(string, delimiter) Splits string on delimiter and returns an array. With limit, only the first limit - 1 delimiters are split upon, thereby returning an array of size at most limit.The last element in the array always contains everything left in the string in the case where there are >= limit occurrences of theHELLO ROCKSET
The opportunity to work on a cloud-native solution is compelling as it provides new ways to make things better, especially when using an LSM. Two examples are RocksDB Cloud and disaggregating LSM compaction. RocksDB Cloud adapts RocksDB to use object storage like S3. The high durability of cloud object storage lets me focus on solving higher OPERATIONAL ANALYTICS: BUILDING LOW-LATENCY QUERIES A time-series database is a specialized operational analytics database. Queries are low latency and it can support high concurrency of queries. Examples of time-series databases are Druid, InfluxDB and TimescaleDB. It can support a complex aggregations on one dimension and that dimension is ‘time’. On the other hand, an OPAP systemcan
BEST PRACTICES FOR ANALYZING KAFKA EVENT STREAMS Conclusion. Building real-time analytics on Kafka event streams involves careful consideration of each of these aspects to ensure the capabilities of the analytics stack meet the requirements of your application and engineering team. Elasticsearch, Druid, Postgres, and Rockset are commonly used as real-time databases to serve analytics ondata
IDENTITY & ACCESS MANAGEMENT Identity & Access Management. This page covers how to manage authentication and authorization in your Rockset organization. # API Keys All requests made to the Rockset API must be authorized with a Rockset API key. API keys can be created in the API Keys tab of the Rockset Console. Once the first API key is created using the Rockset Console, you can use that API key to access the Rockset API APICLIENT - ROCKSET DOCUMENTATION selectHeaderContentType. Select the Content-Type header's value from the given array: if JSON exists in the given array, use it; otherwise use the first one of the array. The Content-Type header to use. If the given array is empty, or matches "any", JSON will be used. This app works best with JavaScript enabled. Product Pricing Resources BlogCompany
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Serverless Search & Analytics Fast SQL on NoSQL data from Kafka, DynamoDB, S3 and more.Get Started Free
Tech Talk: A Data Management System for Low-Latency Queries for Searchand Analytics
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Go from Useful Data to Useful Applications in Minutes Use familiar SQL to ask your data anything, without worrying about the shape of the data or the complexity of your query. Power your data-driven application or interactive dashboard with SQL queries directly on raw data, without managing custom pipelines, servers or databases. Rockset is operational analytics at warp speed.What is Rockset
Rockset is a scalable, reliable search and analytics service in the cloud that makes it easy to build fast operational applications on TBs of data simply using SQL. Rockset delivers millisecond-latency SQL directly on raw data, including nested JSON, XML, Parquet and CSV, without any ETL. Use Rockset to build a Python application that analyzes real-time sensor data. Or an interactive Tableau dashboard that queries operational data from your data lake. Or trigger a lambda function to expose your own data API internally.Why Rockset
A simpler data stack is fundamental to making data usable by developers. Without the burden of ETL, database tuning and server provisioning, teams can direct all their efforts towards building amazing applications, faster. Familiar SQL without any ETL Explore your semi-structured data as a SQL table without defining a schema ahead of time, and run familiar SQL queries (including JOINs) directly from your application or interactive dashboard. see how:A Pittsburgh developer runs SQL on raw JSON weather andpollution data
Fast ad hoc queries
Your data is automatically distributed, replicated and indexed in real-time, so you get millisecond-latency SQL without having to know the shape of your data or type of query before hand. see how:Decore runs SQL queries on DynamoDB in milliseconds Scale without servers Scale your queries to thousands of QPS while your data footprint grows from few GBs to tens of TBs - with no capacity planning, no clusters to manage and no scaling limits. see how:Fynd saves time and energy in analyzing 40 real-time metricsfrom Kafka
How it Works
A new way to load, process and serve data so you can get started in minutes and scale effortlessly. Data generated by users, sensors or applicationsraw data
Raw data captured in your stream, lake, warehouse or operationaldatabase
live sync
Millisecond-latency SQL over TBs of raw data, without any ETLsql over rest
Data-driven application or interactive dashboard Connect securely to your data source Use one of Rockset's native integrations with DynamoDB, Amazon S3, Google Cloud Storage, Amazon Redshift, Kinesis, Apache Kafka and more to securely load your data. Stay in sync with your source without building custom pipelines. Explore semi-structured data as SQL table Your nested JSON, XML, CSV or Parquet dataset is converted into a SQL table WITHOUT REQUIRING A SCHEMA AHEAD OF TIME. Rockset distributes, replicates and indexes your dataset so you can run millisecond-latency SQL (including JOINs) over TBs of data. Query directly from application or dashboard Query Rockset from a Python Flask app. Or from an interactive Tableau dashboard. Or from AWS Lambdas to expose your own data APIs. Rockset auto-scales storage and compute, so you get reliable performance without having to manage servers or databases. Operational Analytics in the Cloud In the digital world, you need to analyze data to trigger actions as changes happen, which typically means you need to query TBs of data in real-time. This is operational analytics, and it demands new capabilities that are not possible with transactional databases ordata warehouses.
Transactional DatabasesData Access
Transactional
Data Scale
Few GBs - Few TBs
Query Latency
Milliseconds
Query Complexity
Low
Concurrency
High
Operational AnalyticsData Access
Real-time
Data Scale
Few GBs - Hundreds of TBsQuery Latency
Milliseconds
Query Complexity
High
Concurrency
High
Data Warehouses
Data Access
Historical
Data Scale
Petabytes
Query Latency
Minutes
Query Complexity
High
Concurrency
Low
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