Apache ignite is one of the powerful horizontally scalable in-memory computing platforms which is capable to handling huge amount of data in memory/disk with quick cluster restart.
In Spark SQL the physical plan provides the fundamental information about the execution of the query. The objective of this talk is to convey understanding and familiarity of query plans in Spark SQL, and use that knowledge to achieve better performance of Apache Spark queries. We will walk you through the most common operators you might find in the query plan and explain some relevant information that can be useful in order to understand some details about the execution. If you understand the query plan, you can look for the weak spot and try to rewrite the query to achieve a more optimal plan that leads to more efficient execution.
The main content of this talk is based on Spark source code but it will reflect some real-life queries that we run while processing data. We will show some examples of query plans and explain how to interpret them and what information can be taken from them. We will also describe what is happening under the hood when the plan is generated focusing mainly on the phase of physical planning. In general, in this talk we want to share what we have learned from both Spark source code and real-life queries that we run in our daily data processing.
Quantitative Performance Evaluation of Cloud-Based MySQL (Relational) Vs. Mon...Darshan Gorasiya
To compare the performance of MySQL (Consistency & Availability - CA) with MongoDB (consistency & partition - CP). Yahoo! Cloud Serving Benchmark (YCSB) automated workloads used for quantitative comparison with large and small data volume.
Performance Analysis and Optimizations for Kafka Streams ApplicationsGuozhang Wang
High-speed and low footprint data stream processing is high in demand for Kafka Streams applications. However, how to write an efficient streaming application using the Streams DSL has been asked by many users in the past since it requires some deep knowledge about Kafka Streams internals. In this talk, I will talk about how to analyze your Kafka Streams applications, target performance bottlenecks and unnecessary storage costs, and optimize your application code accordingly using the Streams DSL.
In addition, I will talk about the new optimization framework that we have been developed inside Kafka Streams since the 2.1 release which replaced the in-place translation of the Streams DSL into a comprehensive process composed of streams topology compilation and rewriting phases, with a focus on reducing various storage footprints of Streams applications, such as state stores, internal topics etc.
A Deep Dive into Query Execution Engine of Spark SQLDatabricks
Spark SQL enables Spark to perform efficient and fault-tolerant relational query processing with analytics database technologies. The relational queries are compiled to the executable physical plans consisting of transformations and actions on RDDs with the generated Java code. The code is compiled to Java bytecode, executed at runtime by JVM and optimized by JIT to native machine code at runtime. This talk will take a deep dive into Spark SQL execution engine. The talk includes pipelined execution, whole-stage code generation, UDF execution, memory management, vectorized readers, lineage based RDD transformation and action.
In Spark SQL the physical plan provides the fundamental information about the execution of the query. The objective of this talk is to convey understanding and familiarity of query plans in Spark SQL, and use that knowledge to achieve better performance of Apache Spark queries. We will walk you through the most common operators you might find in the query plan and explain some relevant information that can be useful in order to understand some details about the execution. If you understand the query plan, you can look for the weak spot and try to rewrite the query to achieve a more optimal plan that leads to more efficient execution.
The main content of this talk is based on Spark source code but it will reflect some real-life queries that we run while processing data. We will show some examples of query plans and explain how to interpret them and what information can be taken from them. We will also describe what is happening under the hood when the plan is generated focusing mainly on the phase of physical planning. In general, in this talk we want to share what we have learned from both Spark source code and real-life queries that we run in our daily data processing.
Quantitative Performance Evaluation of Cloud-Based MySQL (Relational) Vs. Mon...Darshan Gorasiya
To compare the performance of MySQL (Consistency & Availability - CA) with MongoDB (consistency & partition - CP). Yahoo! Cloud Serving Benchmark (YCSB) automated workloads used for quantitative comparison with large and small data volume.
Performance Analysis and Optimizations for Kafka Streams ApplicationsGuozhang Wang
High-speed and low footprint data stream processing is high in demand for Kafka Streams applications. However, how to write an efficient streaming application using the Streams DSL has been asked by many users in the past since it requires some deep knowledge about Kafka Streams internals. In this talk, I will talk about how to analyze your Kafka Streams applications, target performance bottlenecks and unnecessary storage costs, and optimize your application code accordingly using the Streams DSL.
In addition, I will talk about the new optimization framework that we have been developed inside Kafka Streams since the 2.1 release which replaced the in-place translation of the Streams DSL into a comprehensive process composed of streams topology compilation and rewriting phases, with a focus on reducing various storage footprints of Streams applications, such as state stores, internal topics etc.
A Deep Dive into Query Execution Engine of Spark SQLDatabricks
Spark SQL enables Spark to perform efficient and fault-tolerant relational query processing with analytics database technologies. The relational queries are compiled to the executable physical plans consisting of transformations and actions on RDDs with the generated Java code. The code is compiled to Java bytecode, executed at runtime by JVM and optimized by JIT to native machine code at runtime. This talk will take a deep dive into Spark SQL execution engine. The talk includes pipelined execution, whole-stage code generation, UDF execution, memory management, vectorized readers, lineage based RDD transformation and action.
Fungsi Single Row dan Multi Row pada OracleRIZKY ASIAWATI
pada materi ini dibahas tentang fungsi single row dan multi row pada database Oracle , dan multi row (seperti MIN, MAX, SUM dan AVG), disertai contoh dan materi yang bisa dimengerti
C* Summit 2013: The World's Next Top Data Model by Patrick McFadinDataStax Academy
You know you need Cassandra for it's uptime and scaling, but what about that data model? Let's bridge that gap and get you building your game changing app. We'll break down topics like storing objects and indexing for fast retrieval. You will see by understanding a few things about Cassandra internals, you can put your data model in the spotlight. The goal of this talk is to get you comfortable working with data in Cassandra throughout the application lifecycle. What are you waiting for? The cameras are waiting!
Everyday I'm Shuffling - Tips for Writing Better Spark Programs, Strata San J...Databricks
Watch video at: http://youtu.be/Wg2boMqLjCg
Want to learn how to write faster and more efficient programs for Apache Spark? Two Spark experts from Databricks, Vida Ha and Holden Karau, provide some performance tuning and testing tips for your Spark applications
Operating and Supporting Delta Lake in ProductionDatabricks
Delta lake is widely adopted. There are things to be aware of when dealing with petabytes of data in Delta Lake. These smart decisions can give the best efficiency and increase the adoption of Delta. Best practices like OPTIMIZE, ZORDER have to wisely chosen. We have support stories where we successfully resolved performance issues by applying the right performance strategy. There are a set of common issues or repeated questions from our strategic customers face when using Delta and in this session we cover them and how to address them.
Materialized Column: An Efficient Way to Optimize Queries on Nested ColumnsDatabricks
In data warehouse area, it is common to use one or more columns in complex type, such as map, and put many subfields into it. It may impact the query performance dramatically because: 1) It is a waste of IO. The whole column (in map), which may contain tens of subfields, need to be read. And Spark will traverse the whole map and get the value of the target key. 2) Vectorized read can not be exploit when nested type column is read. 3) Filter pushdown can not be utilized when nested columns is read. Over the last year, we have added a series of optimizations in Apache Spark to solve the above problems for Parquet.
2022-06-23 Apache Arrow and DataFusion_ Changing the Game for implementing Da...Andrew Lamb
DataFusion is an extensible and embeddable query engine, written in Rust used to create modern, fast and efficient data pipelines, ETL processes, and database systems.
This presentation explains where it fits into the data eco system and how it helps implement your system in Rust
This is from a 2 hour talk introducing in-memory databases. First a look at traditional RDBMS architecture and some of it's limitations, then a look at some in-memory products and finally a closer look at OrigoDB, the open source in-memory database toolkit for NET/Mono.
Fungsi Single Row dan Multi Row pada OracleRIZKY ASIAWATI
pada materi ini dibahas tentang fungsi single row dan multi row pada database Oracle , dan multi row (seperti MIN, MAX, SUM dan AVG), disertai contoh dan materi yang bisa dimengerti
C* Summit 2013: The World's Next Top Data Model by Patrick McFadinDataStax Academy
You know you need Cassandra for it's uptime and scaling, but what about that data model? Let's bridge that gap and get you building your game changing app. We'll break down topics like storing objects and indexing for fast retrieval. You will see by understanding a few things about Cassandra internals, you can put your data model in the spotlight. The goal of this talk is to get you comfortable working with data in Cassandra throughout the application lifecycle. What are you waiting for? The cameras are waiting!
Everyday I'm Shuffling - Tips for Writing Better Spark Programs, Strata San J...Databricks
Watch video at: http://youtu.be/Wg2boMqLjCg
Want to learn how to write faster and more efficient programs for Apache Spark? Two Spark experts from Databricks, Vida Ha and Holden Karau, provide some performance tuning and testing tips for your Spark applications
Operating and Supporting Delta Lake in ProductionDatabricks
Delta lake is widely adopted. There are things to be aware of when dealing with petabytes of data in Delta Lake. These smart decisions can give the best efficiency and increase the adoption of Delta. Best practices like OPTIMIZE, ZORDER have to wisely chosen. We have support stories where we successfully resolved performance issues by applying the right performance strategy. There are a set of common issues or repeated questions from our strategic customers face when using Delta and in this session we cover them and how to address them.
Materialized Column: An Efficient Way to Optimize Queries on Nested ColumnsDatabricks
In data warehouse area, it is common to use one or more columns in complex type, such as map, and put many subfields into it. It may impact the query performance dramatically because: 1) It is a waste of IO. The whole column (in map), which may contain tens of subfields, need to be read. And Spark will traverse the whole map and get the value of the target key. 2) Vectorized read can not be exploit when nested type column is read. 3) Filter pushdown can not be utilized when nested columns is read. Over the last year, we have added a series of optimizations in Apache Spark to solve the above problems for Parquet.
2022-06-23 Apache Arrow and DataFusion_ Changing the Game for implementing Da...Andrew Lamb
DataFusion is an extensible and embeddable query engine, written in Rust used to create modern, fast and efficient data pipelines, ETL processes, and database systems.
This presentation explains where it fits into the data eco system and how it helps implement your system in Rust
This is from a 2 hour talk introducing in-memory databases. First a look at traditional RDBMS architecture and some of it's limitations, then a look at some in-memory products and finally a closer look at OrigoDB, the open source in-memory database toolkit for NET/Mono.
Overview of MongoDB and Other Non-Relational DatabasesAndrew Kandels
My Minnesota PHP Usergroup (mnphp.org) presentation where I give an overview on MongoDB and other non-relational databases and their ability to solve unique, complex problems.
Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010Bhupesh Bansal
Jan 22nd, 2010 Hadoop meetup presentation on project voldemort and how it plays well with Hadoop at linkedin. The talk focus on Linkedin Hadoop ecosystem. How linkedin manage complex workflows, data ETL , data storage and online serving of 100GB to TB of data.
Scaling JPA applications or deploying them to flexible resources can be a challenge. How do I scale, what is the impact on caching and how can I reuse resources? In this talk we will work through these challenges with real examples using JPA and EclipseLink. Exploring where and when to apply best practices and the many features available for caching, scalability, resource sharing and elastic deployments.
This slide deck provides an introduction to the fundamental concepts and principles of system design. Aimed at aspiring software engineers and professionals looking to strengthen their understanding, the presentation covers key topics such as scalability, reliability, load balancing, caching, CDN, partitioning, indexes, replication etc.
Best Hadoop Institutes : kelly tecnologies is the best Hadoop training Institute in Bangalore.Providing hadoop courses by realtime faculty in Bangalore.
Similar to Apache ignite as in-memory computing platform (20)
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
4. Stream Consuming Application: 1
Cache serves as first data layer
Manage persisting data to database
Processing much faster due to no direct DB access
5. Stream Consuming Application cont…
Cache serves as first class in memory data database
Manage persisting data to native storage
No DB connections, mechanism overhead
7. Cache Evolution
Distributed caches
Shared cache for app instances
Beyond local RAM capacity
Ease of maintenance
No auto sync with DB(yes/no) ?
In App caches
Cache results
More responsive application
Reduce load on DB
Limited to local RAM size
8. Cache Evolution : Data grids
Benefits
Distributed caches with brains
Compute capabilities
DB Read/Write through
Collocated processing
Better scalability
9. Cache Evolution : In memory computing
Memory centric storage
Scalable to store data in TBs
Sql, transactions support
Collocate related data
DB Read/Write through
Pluggable to ext databases
Native storage on disk
No Ram warm up
Compute capabilities
Map Reduce
Collocated processing
Better scalability
10. What is Apache Ignite ?
A distributed cache
A Distributed in memory data grid
A Distributed in memory database
High-performance computing with in-memory
ANSI 99 SQL Compliant
Transactional operations
SQL transactions in beta
11. Ignite cluster
Group of nodes
Types:
Server : stores data, baseline node
Thick client node : doesn’t store data
Thin client node : not part of cluster
Attribute based grouping possible
Scalable
Fault tolerant
Data consistency
Demo
12. Data Grid
Distributed In-Memory Caching
Read/Write through
Data Consistency
Off-Heap Storage
Distributed SQL
ACID Support
Transactions
14. Cache Queries…
Scan Query : Return data matching BiPredicate
Predicate sent to each node,
Node scan its cache
Data consolidated by requested node
Sql Query : load data based on sql given
Needs indexing to be enabled
Registering indexing in config
Annotations for fields visibility
Other queries:
Text Query
Index query
Continuous query
15. Data Partitioning
Partitioned caches
Backups
Ensures data availability in node failures
Read from backup node when primary node leaves
Demo
16. Demo Queries
Scan Query
Sql Query
Data collocation
Next week : this slide onwards
17. Data collocation
Collocate related data for performance
All Employees of dept. can be stored together
Affinity on dept. attribute
Only key attribute can be used in affinity key
Performant CRUD operations
Avoids network trips
Reduced latency
Can cause hot nodes if used inappropriately
18. Compute Tasks
Run distributed computations on grid
Tasks can be run on selected nodes
Ignite manages the task management
E.g. node specific aggregates
List each dept.. students stored on each node
Can be parallelized
19. Continuous Queries
Exactly once processing semantic
3 basic components
Cache to monitor updates
Remote filter to look for data changes
Local listener to act upon data changes
Optional initial query to process initial data
Used to capture data changes on cache
Use case: Reacting to cache entry change
Listen for particular state of cache value
Process the state
Move to next state
20. Eviction Policies
On Heap [cache level]
LRU : Recommended when in doubt
FIFO : It ignores the element access order
Sorted : Sorted according to key for order
Off Heap [data region level]
Random LRU:
Random-2 LRU
Persistence On [Page replacement]
Random-LRU
Segmented-LRU
Clock
22. Data Distribution
Why distributing data ?
Data size can go beyond node limits
Load beyond node processing limits
Solutions:
partition the dataset
Migrate to distributed database
Both will have set of nodes : topology
23. Data Distribution Soln.
Distribution Requirements:
Algorithm
Distribution Uniformity
Minimal disruption
Approaches:
Mod N
Consistent Hashing
Rendezvous(HRW)
24. Data Distribution in Ignite
Mapping partition to node
Rendezvous Hashing
Cluster changes moves partitions
Mapping key to partition
Mod N
Partitions are fixed
1024 by default
25. Data Rebalancing
Used when new node join the grid
In memory grids start rebalancing immediately
Enabled manually when persistence is enabled
Possibly more backups than configured in such scenarios
Rebalance Modes
SYNC: cache calls blocked until rebalancing is completed
ASYNC: rebalancing happen in background. Cache respond immediately
NONE : No rebalancing, cache loaded on demand when required or explicitly loading
26. Partition Map Exchange
Triggered when partitions need to
moved across nodes
A node joins/leaves the cluster
New cache is created/stopped
An index is created etc.
Cluster waits for ongoing
operations
Oldest/youngest node is
coordinator
27. Native Storage Architecture
Work directory
Binary data : internal metadata
Marshaler : marshaler info
DB
Lock file : used to ensure node lock
node dir.(s) : cache partitions
cp dir. (checkpoint start end markers)
WAL dir.
node(s) dir. : wal segments
Archive dir.
Node(s) dir. : wal segments
28. Dirty Pages
Pages are always on disk, optionally in RAM
Each cache update is written to RAM and
appended to WAL
Cache operation cause dirty pages
Dirty pages are accumulated in RAM
Checkpoint: batch of dirty pages written to
disk
WAL file cleared after checkpoint
Updates between checkpoints are logged
Nodes crashes between checkpoints ?
WAL to the rescue
29. Apache Ignite ~ Cassandra
Insert and Update performance is
comparable
Read and mixed(read + update) are 2x+
better in ignite
Cassandra UPADTE outperforms under high
load
Cassandra demands upfront query patterns
Major model changes/new tables if
Query changes required
New queries with different requirements needed
Ignite support collocated/non collocated
joins and hence
Queries can be created just like old school sql
No major changes required except creating few
indexes if needed
Check reference slide for more
30. Next steps
Read docs
Get hands dirty with ignite
Explore queries
Ignite compute tasks
Native persistence
Third party persistence