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Introduction to Dremio
The document outlines Dremio's leadership team and their expertise in modern data analytics, highlighting the complexity and growing demands in the analytics market. It presents Dremio as an innovative solution that allows users to work with any data source and BI tool without the need for ETL processes or data warehouses, making data more accessible. Key features include autonomous data acceleration, improved governance, and self-service data capabilities.
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Introduction to Dremio
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- 3. Tomer Shiran Founder &CEO Previously VP Product, MapR Previously Microsoft; IBM Jacques Nadeau Founder & CTO Recognized SQL & NoSQL expert Founder of Apache Arrow Kelly Stirman CMO Previously VP Strategy, MongoDB Previously Field CTO, MarkLogic Ajay Singh Head of Field Engineering Previously Technical Alliances, Hortonworks Previously Alliances, MarkLogic Ron Avnur VP Engineering Previously VP Product, MongoDB Previously VP Engineering, MarkLogic Collin Weitzman VP Customer Success Previously Sales Executive, Mesosphere Previously Sales Executive, MapR
- 4. Area Dremio TeamMembers Columnar memory Creators of Apache Arrow Columnar storage Creator of Apache Parquet ETL Tech lead of Twitter analytics data pipeline UI UI lead for Apple (iCloud Photos, iTunes U) UX UX lead for Splunk World Class Technical Team Top Silicon Valley VCs
- 5. Our view ofthe market
- 6. Analytics on modern datais incredibly hard Unprecedented complexity
- 7. The demands fordata are growing rapidly Increasing demands Reporting New products Forecasting Threat detection BI Machine Learning Segmenting Fraud prevention
- 8. Your analysts arehungry for data SQL
- 9. Today you engineerdata flows and reshaping Data Staging • Custon ETL • Fragile transforms • Slow moving SQL
- 10. Today you engineerdata flows and reshaping Data Staging Data Warehouse • $$$ • High overhead • Proprietary lock in • Custon ETL • Fragile transforms • Slow moving SQL
- 11. Today you engineerdata flows and reshaping Data Staging Data Warehouse Cubes, BI Extracts & Aggregation Tables • Data sprawl • Governance issues • Slow to update • $$$ • High overhead • Proprietary lock in • Custon ETL • Fragile transforms • Slow moving SQL + + + + + + + + +
- 12. There’s a betterway,
- 13. ✓ Works withany data source ✓ Works with any BI tool ✓ No ETL, no data warehouse, no cubes ✓ Makes data self-service, collaborative ✓ Makes Big Data feel small ✓ Open source There’s a better way,
- 14. Dremio flows, reshapes,and accelerates for you SQL
- 15. Dremio deployment referencearchitectures EC2S3 LDAPLDAP LDAP Parquet Parquet YARNHDFS LDAPLDAP LDAP Parquet Parquet On dedicated infrastructure On Hadoop SQL SQL
- 16. Four key areasexcite customers BI on Modern Data Use any BI tool with Elasticsearch, MongoDB, S3, HDFS, plus joins to relational data Autonomous Data Acceleration Make PB-scale queries fast, without cubes, aggregation tables, or ETL Data Lineage Improve governance with full view of access patterns, data flows, data reshaping, and sharing Self Service Data Empower IT and analysts to discover, curate, accelerate, and share data 1 2 3 4
- 17.
Editor's Notes
- #7 BI assumes single relational database, but… Data in non-relational technologies Data fragmented across many systems Massive scale and velocity
- #8 Data is the business, and… Era of impatient smartphone natives Rise of self-service BI Accelerating time to market Because of the complexity of modern data and increasing demands for data, IT gets crushed in the middle: Slow or non-responsive IT “Shadow Analytics” Data governance risk Illusive data engineers Immature software Competing strategic initiatives
- #9 Here’s the problem everyone is trying to solve today. You have consumers of data with their favorite tools. BI products like Tableau, PowerBI, Qlik, as well as data science tools like Python, R, Spark, and SQL. Then you have all your data, in a mix of relational, NoSQL, Hadoop, and cloud like S3. So how are you going to get the data to the people asking for it?
- #10 Here’s how everyone tries to solve it: First you move the data out of the operational systems into a staging area, that might be Hadoop, or one of the cloud file systems like S3 or Azure Blob Store. You write a bunch of ETL scripts to move the data. These are expensive to write and maintain, and they’re fragile – when the sources change, the scripts have to change too.
- #11 Here’s how everyone tries to solve it: First you move the data out of the operational systems into a staging area, that might be Hadoop, or one of the cloud file systems like S3 or Azure Blob Store. You write a bunch of ETL scripts to move the data. These are expensive to write and maintain, and they’re fragile – when the sources change, the scripts have to change too. Then you move the data into a data warehouse. This could be Redshift, Teradata, Vertica, or other products. These are all proprietary, and they take DBA experts to make them work. And to move the data here you write another set of scripts. But what we see with many customers is that the performance here isn’t sufficient for their needs, and so …
- #12 Here’s how everyone tries to solve it: First you move the data out of the operational systems into a staging area, that might be Hadoop, or one of the cloud file systems like S3 or Azure Blob Store. You write a bunch of ETL scripts to move the data. These are expensive to write and maintain, and they’re fragile – when the sources change, the scripts have to change too. Then you move the data into a data warehouse. This could be Redshift, Teradata, Vertica, or other products. These are all proprietary, and they take DBA experts to make them work. And to move the data here you write another set of scripts. But what we see with many customers is that the performance here isn’t sufficient for their needs, and so … You build cubes and aggregation tables to get the performance your users are asking for. And to do this you build another set of scripts. In the end you’re left with something like this picture. You may have more layers, the technologies may be different, but you’re probably living with something like this. And nobody likes this – it’s expensive, the data movement is slow, it’s hard to change. But worst of all, you’re left with a dynamic where every time a consumer of the data wants a new piece of data: They open a ticket with IT IT begins an engineering project to build another set of pipelines, over several weeks or months
- #13 And so we started Dremio to say, hey, we think there’s a better way to do this.
- #14 And when we got started we asked ourselves, what would we need to do to make this better. And we came up with these requirements. Works with any source. Relational, non-relational, 3rd party apps. 5 years ago nobody was using Hadoop, S3, MongoDB, and 5 years from now there will be new products. You need a solution that is future proof. Works with any BI tool. In every company multiple tools are in use. Each department has their favorite. We need to work with all of them. No ETL, data warehouse, cubes. This would need to give you a really good alternative to these options. Makes data self-service, collaborative. Probably most important of all, we need to change the dynamic between the business and IT. We need to make it so business users can get the data they want, in the shape they want it, without waiting on IT. Makes Big Data feels small. It needs to make billions of rows feel like a spreadsheet on your desktop. Open source. It’s 2017, so we think this has to be open source.
- #15 And that’s Dremio. It sits between all the places you’re creating or capturing data, and all the tools you use to access data. At a high level, that’s how Dremio works. We’ll get into how it works a little later.
- #16 To go one level deeper, Dremio is a distributed process that you run on 1 – 1000+ servers. You can run it on dedicated infrastructure, like you see on the left. Or in your Hadoop cluster, provisioned and managed via YARN. OK, enough with the pictures, let’s get into the demo.
- #17 But one quick question for you. Dremio is a big product, and there are lots of things we could show you, but it would be great to get a little guidance on how to spend our time. When we show Dremio to customers, they tend to get excited by four key areas: