The Modern Data Team for the Modern Data Stack: dbt and the Role of the Analytics Engineer
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A traditional data team has roles including data engineer, data scientist, and data analyst. However, many organizations are finding success by integrating a new role – the analytics engineer. The analytics engineer develops a code-based data infrastructure that can serve both analytics and data science teams. He or she develops re-usable data models using the software engineering practices of version control and unit testing, and provides the critical domain expertise that ensures that data products are relevant and insightful. In this talk we’ll talk about the role and skill set of the analytics engineer, and discuss how dbt, an open source programming environment, empowers anyone with a SQL skillset to fulfill this new role on the data team. We’ll demonstrate how to use dbt to build version-controlled data models on top of Delta Lake, test both the code and our assumptions about the underlying data, and orchestrate complete data pipelines on Apache Spark™.
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Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
Introduction to Data Vault Modeling
Not to be confused with Oracle Database Vault (a commercial db security product), Data Vault Modeling is a specific data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It is not a replacement for star schema data marts (and should not be used as such). This approach has been used in projects around the world (Europe, Australia, USA) for the last 10 years but is still not widely known or understood. The purpose of this presentation is to provide attendees with a detailed introduction to the technical components of the Data Vault Data Model, what they are for and how to build them. The examples will give attendees the basics for how to build, and design structures when using the Data Vault modeling technique. The target audience is anyone wishing to explore implementing a Data Vault style data model for an Enterprise Data Warehouse, Operational Data Warehouse, or Dynamic Data Integration Store. See more content like this by following my blog http://kentgraziano.com or follow me on twitter @kentgraziano.
Presto: Fast SQL-on-Anything (including Delta Lake, Snowflake, Elasticsearch ...
Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Comcast, GrubHub, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores.
Airbyte @ Airflow Summit - The new modern data stack
In this talk, I’ll describe how you can leverage 3 open-source standards - workflow management with Airflow, EL with Airbyte, transformation with dbt - to build your next modern data stack. I’ll explain how to configure your Airflow DAG to trigger Airbyte’s data replication jobs and DBT’s transformation one with a concrete use case.
Data Lakehouse, Data Mesh, and Data Fabric (r1)
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Databricks: A Tool That Empowers You To Do More With Data
In this talk we will present how Databricks has enabled the author to achieve more with data, enabling one person to build a coherent data project with data engineering, analysis and science components, with better collaboration, better productionalization methods, with larger datasets and faster.
The talk will include a demo that will illustrate how the multiple functionalities of Databricks help to build a coherent data project with Databricks jobs, Delta Lake and auto-loader for data engineering, SQL Analytics for Data Analysis, Spark ML and MLFlow for data science, and Projects for collaboration.
Growing the Delta Ecosystem to Rust and Python with Delta-RS
In this session we will introduce the delta-rs project which is helping bring the power of Delta Lake outside of the Spark ecosystem. By providing a foundational Delta Lake library in Rust, delta-rs can enable native bindings in Python, Ruby, Golang, and more.We will review what functionality delta-rs supports in its current Rust and Python APIs and the upcoming roadmap.
We will also give an overview of one of the first projects to use it in production: kafka-delta-ingest, which builds on delta-rs to provide a high throughput service to bring data from Kafka into Delta Lake.
Delta lake and the delta architecture
Slides from the July 2021 St. Louis Big Data IDEA meetup. Yue Fang presented on Delta Lake.
Large Scale Lakehouse Implementation Using Structured Streaming
Business leads, executives, analysts, and data scientists rely on up-to-date information to make business decision, adjust to the market, meet needs of their customers or run effective supply chain operations.
Come hear how Asurion used Delta, Structured Streaming, AutoLoader and SQL Analytics to improve production data latency from day-minus-one to near real time Asurion’s technical team will share battle tested tips and tricks you only get with certain scale. Asurion data lake executes 4000+ streaming jobs and hosts over 4000 tables in production Data Lake on AWS.
[DSC Europe 22] Lakehouse architecture with Delta Lake and Databricks - Draga...
[DSC Europe 22] Lakehouse architecture with Delta Lake and Databricks - Draga...DataScienceConferenc1
Dragan Berić will take a deep dive into Lakehouse architecture, a game-changing concept bridging the best elements of data lake and data warehouse. The presentation will focus on the Delta Lake format as the foundation of the Lakehouse philosophy, and Databricks as the primary platform for its implementation.What's hot (20)
Snowflake: The most cost-effective agile and scalable data warehouse ever!
Snowflake: The most cost-effective agile and scalable data warehouse ever!
Build Real-Time Applications with Databricks Streaming
Build Real-Time Applications with Databricks Streaming
Architect’s Open-Source Guide for a Data Mesh Architecture
Architect’s Open-Source Guide for a Data Mesh Architecture
Presto: Fast SQL-on-Anything (including Delta Lake, Snowflake, Elasticsearch ...
Presto: Fast SQL-on-Anything (including Delta Lake, Snowflake, Elasticsearch ...
Airbyte @ Airflow Summit - The new modern data stack
Airbyte @ Airflow Summit - The new modern data stack
Databricks: A Tool That Empowers You To Do More With Data
Databricks: A Tool That Empowers You To Do More With Data
Growing the Delta Ecosystem to Rust and Python with Delta-RS
Growing the Delta Ecosystem to Rust and Python with Delta-RS
Large Scale Lakehouse Implementation Using Structured Streaming
Large Scale Lakehouse Implementation Using Structured Streaming
[DSC Europe 22] Lakehouse architecture with Delta Lake and Databricks - Draga...
[DSC Europe 22] Lakehouse architecture with Delta Lake and Databricks - Draga...
Similar to The Modern Data Team for the Modern Data Stack: dbt and the Role of the Analytics Engineer
Lessons from Building Large-Scale, Multi-Cloud, SaaS Software at Databricks
The cloud has become one of the most attractive ways for enterprises to purchase software, but it requires building products in a very different way from traditional software
Simplifying AI integration on Apache Spark
Spark is an ETL and Data Processing engine especially suited for big data. Most of the time an organization has different teams working on different languages, frameworks and libraries, which needs to be integrated in the ETL Pipelines or for general data processing. For example, a Spark ETL job may be written in Scala by data engineering team, but there is a need to integrate a machine learning solution written in python/R developed by Data Science team. These kinds of solutions are not very straightforward to integrate with spark engine, and it required great amount of collaboration between different teams, hence increasing overall project time and cost. Furthermore, these solutions will keep on changing/upgrading with time using latest versions of the technologies and with improved design and implementation, especially in Machine Learning domain where ML models/algorithms keep on improving with new data and new approaches. And so there is significant downtime involved in integrating the these upgraded version.
Data Versioning and Reproducible ML with DVC and MLflow
Machine Learning development involves comparing models and storing the artifacts they produced. We often compare several algorithms to select the most efficient ones. We assess different hyper-parameters to fine-tune the model. Git helps us store multiple versions of our code. Additionally, we need to keep track of the datasets we are using. This is important not only for audit purposes but also for assessing the performances of the models, developed at a later time. Git is a standard code versioning tool in software development. It can be used to store your datasets but it does not offer an optimal solution.
DWX 2023 - Datenbank-Schema Deployment im Kubernetes Release
Kubernetes bietet viel Funktionalität, um Zero-Downtime Deployments durchzuführen. Etwas herausfordernder wird es dann, wenn der Service-Update auch mit einem Datenbank-Schema Update verbunden ist. Nebst den verschiedenen Strategien, um ein Datenbankschema in einem Zero-Downtime-Release auszurollen, lernen Sie in diesem Vortrag, wie das Datenbank-Schema sowie die Deployment-Tools in einem Container Verpackt mit der Applikation ausgerollt werden können. Somit erhalten wir ein einziges, in sich konsistentes, Helm Paket, welches den Service samt Datenbank-Schema ausrollen kann.
Data Con LA 2022 - Pre- Recorded - Simplifying AI/ML using Databricks feature...
Debu Sinha, Sr Specialist Solutions Architect - AI/ML at Databricks
AI/ ML/ Data Science
1. What are feature stores. 2. Why are they important? 3. Using Databricks and the feature store offering to streamline ml. This hold true for small companies as well. How we frame our approach to AI initiatives will determine its success. Don't worry, I am not a zealot. I will not tell you AI and ML are the cure-all and will solve all your problems. Some tasks are particularly well suited to these techniques, but not all. What I love about them is the fact that they allow us to tackle difficult problems that might otherwise be too daunting.
COSCUP 2019 - The discussion between Knex.js and PostgreSQL
The discussion between knex.js and postgreSQL
New Developments in Spark
Matei Zaharia (CTO Databricks, creator of Apache Spark) shares new developments in the open source project.
In-Memory Computing - The Big Picture
In-memory computing is ultra-fast and offers completely new possibilities. Let‘s analyze which factors slow down classic JPA apps, why NoSQL isn't more effective, how we can optimize JPA performance, and where are the limits are.
After that, you will learn which in-memory strategies you can choose to speed up your performance. Let's have a look at in-memory databases like Times-Ten, in-memory grids like Coherence, and popular caching frameworks.
After that, you will learn which in-memory strategies you can choose to speed up your apps. We will have a look at in-memory databases like Times-Ten, in-memory grids like Coherence, and caching frameworks.
Finally, we introduce you to the pure Java in-memory computing paradigm. You will learn how you can build up Java in-memory database apps, how you can execute queries in microseconds or even nanoseconds, and how you can persist your data on disk. No magic, but pure Java and JVM-power only.
DSDT Meetup Nov 2017
Machine Learning through Google Cloud Platform’s Dataflow
Agenda:
6:00 - 6:15: Welcome
6:15 – 7:00: JDA Labs - Machine Learning through Google Cloud Platform’s Dataflow
7:00- 7:30 : Google - Building a Dataflow streaming pipeline for sentiment analysis on Twitter
7:30 - 8:00: Networking + Q&A
How R Developers Can Build and Share Data and AI Applications that Scale with...
Historically it has been challenging for R developers to build and share data products that use Apache Spark. In this talk, learn how you can publish Shiny apps that leverage the scale and speed of Databricks, Spark and Delta Lake, so your stakeholders can better leverage insights from your data in their decision making.
.NET per la Data Science e oltre
Come può .NET contribuire alla Data Science? Cosa è .NET Interactive? Cosa c'entrano i notebook? E Apache Spark? E il pythonismo? E Azure? Vediamo in questa sessione di mettere in ordine le idee.
Resume 11 2015
• A competent professional with 3.5 years of experience in Data warehousing and Investment Banking Domain.
• Expertise in end-to-end implementation of various projects including designing, development, coding, implementation of software applications.
Very large scale distributed deep learning on BigDL
Presentation on how to bring Deep Learning To Big Data Platform.
If you like what you read be sure you ♥ it below. Thank you!
How Service Mesh Fits into the Modern Data Stack
The modern data stack has become increasingly popular in the analytics community. Patterns like domain-driven design, known from classical software development, are finding their way into analytics contexts. This is the basis of a new paradigm, like Data Mesh. In a Data Mesh, every domain - like a different department for example - wants to solve similar problems with their own business data. Therefore, it’s vital to implement a flexible, lightweight, and manageable, but also secured and monitorable central self-service data platform. With the containerization of services, and using Kubernetes as a runtime, you can build flexible data architectures. Data visualization, data ingestion, orchestration, and ETL tools, as well as Cloud Data Warehouses, should all live together in a kind of a mesh. In this session, learn how Kong's CNCF Sandbox, project Kuma, provides the next level of security when handling data, other business domains, and exchanging data with external systems. Uncover the advantages of end-to-end tracing, data collection, and external access from outside of the mesh using Data APIs.
Similar to The Modern Data Team for the Modern Data Stack: dbt and the Role of the Analytics Engineer (20)
Lessons from Building Large-Scale, Multi-Cloud, SaaS Software at Databricks
Lessons from Building Large-Scale, Multi-Cloud, SaaS Software at Databricks
Data Versioning and Reproducible ML with DVC and MLflow
Data Versioning and Reproducible ML with DVC and MLflow
DWX 2023 - Datenbank-Schema Deployment im Kubernetes Release
DWX 2023 - Datenbank-Schema Deployment im Kubernetes Release
Data Con LA 2022 - Pre- Recorded - Simplifying AI/ML using Databricks feature...
Data Con LA 2022 - Pre- Recorded - Simplifying AI/ML using Databricks feature...
COSCUP 2019 - The discussion between Knex.js and PostgreSQL
COSCUP 2019 - The discussion between Knex.js and PostgreSQL
How R Developers Can Build and Share Data and AI Applications that Scale with...
How R Developers Can Build and Share Data and AI Applications that Scale with...
Very large scale distributed deep learning on BigDL
Very large scale distributed deep learning on BigDL
More from Databricks
Data Lakehouse Symposium | Day 1 | Part 1
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Data Lakehouse Symposium | Day 1 | Part 2
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Data Lakehouse Symposium | Day 2
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Data Lakehouse Symposium | Day 4
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
In this session, learn how to quickly supplement your on-premises Hadoop environment with a simple, open, and collaborative cloud architecture that enables you to generate greater value with scaled application of analytics and AI on all your data. You will also learn five critical steps for a successful migration to the Databricks Lakehouse Platform along with the resources available to help you begin to re-skill your data teams.
Democratizing Data Quality Through a Centralized Platform
Bad data leads to bad decisions and broken customer experiences. Organizations depend on complete and accurate data to power their business, maintain efficiency, and uphold customer trust. With thousands of datasets and pipelines running, how do we ensure that all data meets quality standards, and that expectations are clear between producers and consumers? Investing in shared, flexible components and practices for monitoring data health is crucial for a complex data organization to rapidly and effectively scale.
At Zillow, we built a centralized platform to meet our data quality needs across stakeholders. The platform is accessible to engineers, scientists, and analysts, and seamlessly integrates with existing data pipelines and data discovery tools. In this presentation, we will provide an overview of our platform’s capabilities, including:
Giving producers and consumers the ability to define and view data quality expectations using a self-service onboarding portal
Performing data quality validations using libraries built to work with spark
Dynamically generating pipelines that can be abstracted away from users
Flagging data that doesn’t meet quality standards at the earliest stage and giving producers the opportunity to resolve issues before use by downstream consumers
Exposing data quality metrics alongside each dataset to provide producers and consumers with a comprehensive picture of health over time
Why APM Is Not the Same As ML Monitoring
Application performance monitoring (APM) has become the cornerstone of software engineering allowing engineering teams to quickly identify and remedy production issues. However, as the world moves to intelligent software applications that are built using machine learning, traditional APM quickly becomes insufficient to identify and remedy production issues encountered in these modern software applications.
As a lead software engineer at NewRelic, my team built high-performance monitoring systems including Insights, Mobile, and SixthSense. As I transitioned to building ML Monitoring software, I found the architectural principles and design choices underlying APM to not be a good fit for this brand new world. In fact, blindly following APM designs led us down paths that would have been better left unexplored.
In this talk, I draw upon my (and my team’s) experience building an ML Monitoring system from the ground up and deploying it on customer workloads running large-scale ML training with Spark as well as real-time inference systems. I will highlight how the key principles and architectural choices of APM don’t apply to ML monitoring. You’ll learn why, understand what ML Monitoring can successfully borrow from APM, and hear what is required to build a scalable, robust ML Monitoring architecture.
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
Autonomy and ownership are core to working at Stitch Fix, particularly on the Algorithms team. We enable data scientists to deploy and operate their models independently, with minimal need for handoffs or gatekeeping. By writing a simple function and calling out to an intuitive API, data scientists can harness a suite of platform-provided tooling meant to make ML operations easy. In this talk, we will dive into the abstractions the Data Platform team has built to enable this. We will go over the interface data scientists use to specify a model and what that hooks into, including online deployment, batch execution on Spark, and metrics tracking and visualization.
Stage Level Scheduling Improving Big Data and AI Integration
In this talk, I will dive into the stage level scheduling feature added to Apache Spark 3.1. Stage level scheduling extends upon Project Hydrogen by improving big data ETL and AI integration and also enables multiple other use cases. It is beneficial any time the user wants to change container resources between stages in a single Apache Spark application, whether those resources are CPU, Memory or GPUs. One of the most popular use cases is enabling end-to-end scalable Deep Learning and AI to efficiently use GPU resources. In this type of use case, users read from a distributed file system, do data manipulation and filtering to get the data into a format that the Deep Learning algorithm needs for training or inference and then sends the data into a Deep Learning algorithm. Using stage level scheduling combined with accelerator aware scheduling enables users to seamlessly go from ETL to Deep Learning running on the GPU by adjusting the container requirements for different stages in Spark within the same application. This makes writing these applications easier and can help with hardware utilization and costs.
There are other ETL use cases where users want to change CPU and memory resources between stages, for instance there is data skew or perhaps the data size is much larger in certain stages of the application. In this talk, I will go over the feature details, cluster requirements, the API and use cases. I will demo how the stage level scheduling API can be used by Horovod to seamlessly go from data preparation to training using the Tensorflow Keras API using GPUs.
The talk will also touch on other new Apache Spark 3.1 functionality, such as pluggable caching, which can be used to enable faster dataframe access when operating from GPUs.
Simplify Data Conversion from Spark to TensorFlow and PyTorch
In this talk, I would like to introduce an open-source tool built by our team that simplifies the data conversion from Apache Spark to deep learning frameworks.
Imagine you have a large dataset, say 20 GBs, and you want to use it to train a TensorFlow model. Before feeding the data to the model, you need to clean and preprocess your data using Spark. Now you have your dataset in a Spark DataFrame. When it comes to the training part, you may have the problem: How can I convert my Spark DataFrame to some format recognized by my TensorFlow model?
The existing data conversion process can be tedious. For example, to convert an Apache Spark DataFrame to a TensorFlow Dataset file format, you need to either save the Apache Spark DataFrame on a distributed filesystem in parquet format and load the converted data with third-party tools such as Petastorm, or save it directly in TFRecord files with spark-tensorflow-connector and load it back using TFRecordDataset. Both approaches take more than 20 lines of code to manage the intermediate data files, rely on different parsing syntax, and require extra attention for handling vector columns in the Spark DataFrames. In short, all these engineering frictions greatly reduced the data scientists’ productivity.
The Databricks Machine Learning team contributed a new Spark Dataset Converter API to Petastorm to simplify these tedious data conversion process steps. With the new API, it takes a few lines of code to convert a Spark DataFrame to a TensorFlow Dataset or a PyTorch DataLoader with default parameters.
In the talk, I will use an example to show how to use the Spark Dataset Converter to train a Tensorflow model and how simple it is to go from single-node training to distributed training on Databricks.
Scaling your Data Pipelines with Apache Spark on Kubernetes
There is no doubt Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark has evolved to run both Machine Learning and large scale analytics workloads. There is growing interest in running Apache Spark natively on Kubernetes. By combining the flexibility of Kubernetes and scalable data processing with Apache Spark, you can run any data and machine pipelines on this infrastructure while effectively utilizing resources at disposal.
In this talk, Rajesh Thallam and Sougata Biswas will share how to effectively run your Apache Spark applications on Google Kubernetes Engine (GKE) and Google Cloud Dataproc, orchestrate the data and machine learning pipelines with managed Apache Airflow on GKE (Google Cloud Composer). Following topics will be covered: – Understanding key traits of Apache Spark on Kubernetes- Things to know when running Apache Spark on Kubernetes such as autoscaling- Demonstrate running analytics pipelines on Apache Spark orchestrated with Apache Airflow on Kubernetes cluster.
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
Pipelines have become ubiquitous, as the need for stringing multiple functions to compose applications has gained adoption and popularity. Common pipeline abstractions such as “fit” and “transform” are even shared across divergent platforms such as Python Scikit-Learn and Apache Spark.
Scaling pipelines at the level of simple functions is desirable for many AI applications, however is not directly supported by Ray’s parallelism primitives. In this talk, Raghu will describe a pipeline abstraction that takes advantage of Ray’s compute model to efficiently scale arbitrarily complex pipeline workflows. He will demonstrate how this abstraction cleanly unifies pipeline workflows across multiple platforms such as Scikit-Learn and Spark, and achieves nearly optimal scale-out parallelism on pipelined computations.
Attendees will learn how pipelined workflows can be mapped to Ray’s compute model and how they can both unify and accelerate their pipelines with Ray.
Sawtooth Windows for Feature Aggregations
In this talk about zipline, we will introduce a new type of windowing construct called a sawtooth window. We will describe various properties about sawtooth windows that we utilize to achieve online-offline consistency, while still maintaining high-throughput, low-read latency and tunable write latency for serving machine learning features.We will also talk about a simple deployment strategy for correcting feature drift – due operations that are not “abelian groups”, that operate over change data.
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
We want to present multiple anti patterns utilizing Redis in unconventional ways to get the maximum out of Apache Spark.All examples presented are tried and tested in production at Scale at Adobe. The most common integration is spark-redis which interfaces with Redis as a Dataframe backing Store or as an upstream for Structured Streaming. We deviate from the common use cases to explore where Redis can plug gaps while scaling out high throughput applications in Spark.
Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue
· Why?
o Custom queries on top a table; We load the data once and query N times
· Why not Structured Streaming
· Working Solution using Redis
Niche 2 : Distributed Counters
· Problems with Spark Accumulators
· Utilize Redis Hashes as distributed counters
· Precautions for retries and speculative execution
· Pipelining to improve performance
Re-imagine Data Monitoring with whylogs and Spark
In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. To address these challenges, we built whylogs, an open source standard for data logging. It is a lightweight data profiling library that enables end-to-end data profiling across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It can work with different modes of data operations, including streaming, batch and IoT data.
In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and various integrations. We will demonstrate how the whylogs integration with Apache Spark achieves large scale data profiling, and we will show how users can apply this integration into existing data and ML pipelines.
Raven: End-to-end Optimization of ML Prediction Queries
Machine learning (ML) models are typically part of prediction queries that consist of a data processing part (e.g., for joining, filtering, cleaning, featurization) and an ML part invoking one or more trained models. In this presentation, we identify significant and unexplored opportunities for optimization. To the best of our knowledge, this is the first effort to look at prediction queries holistically, optimizing across both the ML and SQL components.
We will present Raven, an end-to-end optimizer for prediction queries. Raven relies on a unified intermediate representation that captures both data processing and ML operators in a single graph structure.
This allows us to introduce optimization rules that
(i) reduce unnecessary computations by passing information between the data processing and ML operators
(ii) leverage operator transformations (e.g., turning a decision tree to a SQL expression or an equivalent neural network) to map operators to the right execution engine, and
(iii) integrate compiler techniques to take advantage of the most efficient hardware backend (e.g., CPU, GPU) for each operator.
We have implemented Raven as an extension to Spark’s Catalyst optimizer to enable the optimization of SparkSQL prediction queries. Our implementation also allows the optimization of prediction queries in SQL Server. As we will show, Raven is capable of improving prediction query performance on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. For complex models, where GPU acceleration is beneficial, Raven provides up to 8x speedup compared to state-of-the-art systems. As part of the presentation, we will also give a demo showcasing Raven in action.
Processing Large Datasets for ADAS Applications using Apache Spark
Semantic segmentation is the classification of every pixel in an image/video. The segmentation partitions a digital image into multiple objects to simplify/change the representation of the image into something that is more meaningful and easier to analyze [1][2]. The technique has a wide variety of applications ranging from perception in autonomous driving scenarios to cancer cell segmentation for medical diagnosis.
Exponential growth in the datasets that require such segmentation is driven by improvements in the accuracy and quality of the sensors generating the data extending to 3D point cloud data. This growth is further compounded by exponential advances in cloud technologies enabling the storage and compute available for such applications. The need for semantically segmented datasets is a key requirement to improve the accuracy of inference engines that are built upon them.
Streamlining the accuracy and efficiency of these systems directly affects the value of the business outcome for organizations that are developing such functionalities as a part of their AI strategy.
This presentation details workflows for labeling, preprocessing, modeling, and evaluating performance/accuracy. Scientists and engineers leverage domain-specific features/tools that support the entire workflow from labeling the ground truth, handling data from a wide variety of sources/formats, developing models and finally deploying these models. Users can scale their deployments optimally on GPU-based cloud infrastructure to build accelerated training and inference pipelines while working with big datasets. These environments are optimized for engineers to develop such functionality with ease and then scale against large datasets with Spark-based clusters on the cloud.
Massive Data Processing in Adobe Using Delta Lake
At Adobe Experience Platform, we ingest TBs of data every day and manage PBs of data for our customers as part of the Unified Profile Offering. At the heart of this is a bunch of complex ingestion of a mix of normalized and denormalized data with various linkage scenarios power by a central Identity Linking Graph. This helps power various marketing scenarios that are activated in multiple platforms and channels like email, advertisements etc. We will go over how we built a cost effective and scalable data pipeline using Apache Spark and Delta Lake and share our experiences.
What are we storing?
Multi Source – Multi Channel Problem
Data Representation and Nested Schema Evolution
Performance Trade Offs with Various formats
Go over anti-patterns used
(String FTW)
Data Manipulation using UDFs
Writer Worries and How to Wipe them Away
Staging Tables FTW
Datalake Replication Lag Tracking
Performance Time!
Machine Learning CI/CD for Email Attack Detection
Detecting advanced email attacks at scale is a challenging ML problem, particularly due to the rarity of attacks, adversarial nature of the problem, and scale of data. In order to move quickly and adapt to the newest threat we needed to build a Continuous Integration / Continuous Delivery pipeline for the entire ML detection stack. Our goal is to enable detection engineers and data scientists to make changes to any part of the stack including joined datasets for hydration, feature extraction code, detection logic, and develop/train ML models.
In this talk, we discuss why we decided to build this pipeline, how it is used to accelerate development and ensure quality, and dive into the nitty-gritty details of building such a system on top of an Apache Spark + Databricks stack.
Jeeves Grows Up: An AI Chatbot for Performance and Quality
Sarah: CEO-Finance-Report pipeline seems to be slow today. Why
Jeeves: SparkSQL query dbt_fin_model in CEO-Finance-Report is running 53% slower on 2/28/2021. Data skew issue detected. Issue has not been seen in last 90 days.
Jeeves: Adding 5 more nodes to cluster recommended for CEO-Finance-Report to finish in its 99th percentile time of 5.2 hours.
Who is Jeeves? An experienced Spark developer? A seasoned administrator? No, Jeeves is a chatbot created to simplify data operations management for enterprise Spark clusters. This chatbot is powered by advanced AI algorithms and an intuitive conversational interface that together provide answers to get users in and out of problems quickly. Instead of being stuck to screens displaying logs and metrics, users can now have a more refreshing experience via a two-way conversation with their own personal Spark expert.
We presented Jeeves at Spark Summit 2019. In the two years since, Jeeves has grown up a lot. Jeeves can now learn continuously as telemetry information streams in from more and more applications, especially SQL queries. Jeeves now “knows” about data pipelines that have many components. Jeeves can also answer questions about data quality in addition to performance, cost, failures, and SLAs. For example:
Tom: I am not seeing any data for today in my Campaign Metrics Dashboard.
Jeeves: 3/5 validations failed on the cmp_kpis table on 2/28/2021. Run of pipeline cmp_incremental_daily failed on 2/28/2021.
This talk will give an overview of the newer capabilities of the chatbot, and how it now fits in a modern data stack with the emergence of new data roles like analytics engineers and machine learning engineers. You will learn how to build chatbots that tackle your complex data operations challenges.
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Opendatabay - Open Data Marketplace.pptx
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
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一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单
UMich毕业证【微信95270640】办理UMich毕业证【Q微信95270640】密歇根大学|安娜堡分校毕业证书原版↑制作密歇根大学|安娜堡分校学历认证文凭办理密歇根大学|安娜堡分校留信网认证,留学回国办理毕业证成绩单文凭学历认证【Q微信95270640】专业为海外学子办理毕业证成绩单、文凭制作，学历仿制，回国人员证明、做文凭，研究生、本科、硕士学历认证、留信认证、结业证、学位证书样本、美国教育部认证百分百真实存档可查】
【本科硕士】密歇根大学|安娜堡分校密歇根大学|安娜堡分校硕士毕业证成绩单（GPA修改）；学历认证（教育部认证）；大学Offer录取通知书留信认证使馆认证；雅思语言证书等高仿类证书。
办理流程：
1客户提供办理密歇根大学|安娜堡分校密歇根大学|安娜堡分校硕士毕业证成绩单信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
真实网上可查的证明材料
1教育部学历学位认证留服官网真实存档可查永久存档。
2留学回国人员证明（使馆认证）使馆网站真实存档可查。
我们对海外大学及学院的毕业证成绩单所使用的材料尺寸大小防伪结构（包括：密歇根大学|安娜堡分校密歇根大学|安娜堡分校硕士毕业证成绩单隐形水印阴影底纹钢印LOGO烫金烫银LOGO烫金烫银复合重叠。文字图案浮雕激光镭射紫外荧光温感复印防伪）都有原版本文凭对照。质量得到了广大海外客户群体的认可同时和海外学校留学中介做到与时俱进及时掌握各大院校的（毕业证成绩单资格证结业证录取通知书在读证明等相关材料）的版本更新信息能够在第一时间掌握最新的海外学历文凭的样版尺寸大小纸张材质防伪技术等等并在第一时间收集到原版实物以求达到客户的需求。
本公司还可以按照客户原版印刷制作且能够达到客户理想的要求。有需要办理证件的客户请联系我们在线客服中心微信：95270640 或咨询在线在我脑海中回旋很幸运我有这样的机会能将埋在心底深深的感激之情得以表达从婴儿的“哇哇坠地到哺育我长大您们花去了多少的心血与汗水编织了多少个日日夜夜从上小学到初中乃至大学燃烧着自己默默奉献着光和热全天下的父母都是仁慈的无私的伟大的所以无论何时何地都不要忘记您们是这样的无私将一份沉甸甸的爱和希望传递到我们的眼里和心中而我应做的就是感恩父母回报社会共创和谐父母对我们的恩情深厚而无私孝敬父母是我们做人元
一比一原版(CU毕业证)卡尔顿大学毕业证成绩单
CU毕业证【微信95270640】（卡尔顿大学毕业证成绩单本科学历）Q微信95270640(补办CU学位文凭证书)卡尔顿大学留信网学历认证怎么办理卡尔顿大学毕业证成绩单精仿本科学位证书硕士文凭证书认证Seneca College diplomaoffer,Transcript办理硕士学位证书造假卡尔顿大学假文凭学位证书制作CU本科毕业证书硕士学位证书精仿卡尔顿大学学历认证成绩单修改制作，办理真实认证、留信认证、使馆公证、购买成绩单，购买假文凭，购买假学位证，制造假国外大学文凭、毕业公证、毕业证明书、录取通知书、Offer、在读证明、雅思托福成绩单、假文凭、假毕业证、请假条、国际驾照、网上存档可查！
如果您是以下情况，我们都能竭诚为您解决实际问题：【公司采用定金+余款的付款流程，以最大化保障您的利益，让您放心无忧】
1、在校期间，因各种原因未能顺利毕业，拿不到官方毕业证+微信95270640
2、面对父母的压力，希望尽快拿到卡尔顿大学卡尔顿大学毕业证文凭证书；
3、不清楚流程以及材料该如何准备卡尔顿大学卡尔顿大学毕业证文凭证书；
4、回国时间很长，忘记办理；
5、回国马上就要找工作，办给用人单位看；
6、企事业单位必须要求办理的；
面向美国乔治城大学毕业留学生提供以下服务:
【★卡尔顿大学卡尔顿大学毕业证文凭证书毕业证、成绩单等全套材料，从防伪到印刷，从水印到钢印烫金，与学校100%相同】
【★真实使馆认证（留学人员回国证明），使馆存档可通过大使馆查询确认】
【★真实教育部认证，教育部存档，教育部留服网站可查】
【★真实留信认证，留信网入库存档，可查卡尔顿大学卡尔顿大学毕业证文凭证书】
我们从事工作十余年的有着丰富经验的业务顾问，熟悉海外各国大学的学制及教育体系，并且以挂科生解决毕业材料不全问题为基础，为客户量身定制1对1方案，未能毕业的回国留学生成功搭建回国顺利发展所需的桥梁。我们一直努力以高品质的教育为起点，以诚信、专业、高效、创新作为一切的行动宗旨，始终把“诚信为主、质量为本、客户第一”作为我们全部工作的出发点和归宿点。同时为海内外留学生提供大学毕业证购买、补办成绩单及各类分数修改等服务；归国认证方面，提供《留信网入库》申请、《国外学历学位认证》申请以及真实学籍办理等服务，帮助众多莘莘学子实现了一个又一个梦想。
专业服务，请勿犹豫联系我
如果您真实毕业回国，对于学历认证无从下手，请联系我，我们免费帮您递交
诚招代理：本公司诚聘当地代理人员，如果你有业余时间，或者你有同学朋友需要，有兴趣就请联系我
你赢我赢，共创双赢
你做代理，可以帮助卡尔顿大学同学朋友
你做代理，可以拯救卡尔顿大学失足青年
你做代理，可以挽救卡尔顿大学一个个人才
你做代理，你将是别人人生卡尔顿大学的转折点
你做代理，可以改变自己，改变他人，给他人和自己一个机会他交友与城里人交友但他俩就好像是两个世界里的人根本拢不到一块儿不知不觉山娃倒跟周围出租屋里的几个小伙伴成了好朋友因为他们也是从乡下进城过暑假的小学生快乐的日子总是过得飞快山娃尚未完全认清那几位小朋友时他们却一个接一个地回家了山娃这时才恍然发现二个月的暑假已转到了尽头他的城市生活也将划上一个不很圆满的句号了值得庆幸的是山娃早记下了他们的学校和联系方式说也奇怪在山娃离城的头一天父亲居然请假陪山娃耍了活
一比一原版(NYU毕业证)纽约大学毕业证成绩单
NYU毕业证【微信95270640】《如何办理NYU毕业证纽约大学文凭学历》【Q微信95270640】《纽约大学文凭学历证书》《纽约大学毕业证书与成绩单样本图片》毕业证书补办 Fake Degree做学费单《毕业证明信-推荐信》成绩单，录取通知书，Offer，在读证明，雅思托福成绩单，真实大使馆教育部认证，回国人员证明，留信网认证。网上存档永久可查！
【本科硕士】纽约大学纽约大学毕业证学位证（GPA修改）；学历认证（教育部认证）；大学Offer录取通知书留信认证使馆认证；雅思语言证书等高仿类证书。
办理流程：
1客户提供办理纽约大学纽约大学毕业证学位证信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
真实网上可查的证明材料
1教育部学历学位认证留服官网真实存档可查永久存档。
2留学回国人员证明（使馆认证）使馆网站真实存档可查。
我们对海外大学及学院的毕业证成绩单所使用的材料尺寸大小防伪结构（包括：纽约大学纽约大学毕业证学位证隐形水印阴影底纹钢印LOGO烫金烫银LOGO烫金烫银复合重叠。文字图案浮雕激光镭射紫外荧光温感复印防伪）都有原版本文凭对照。质量得到了广大海外客户群体的认可同时和海外学校留学中介做到与时俱进及时掌握各大院校的（毕业证成绩单资格证结业证录取通知书在读证明等相关材料）的版本更新信息能够在第一时间掌握最新的海外学历文凭的样版尺寸大小纸张材质防伪技术等等并在第一时间收集到原版实物以求达到客户的需求。
本公司还可以按照客户原版印刷制作且能够达到客户理想的要求。有需要办理证件的客户请联系我们在线客服中心微信：95270640 或咨询在线已转到了尽头他的城市生活也将划上一个不很圆满的句号了值得庆幸的是山娃早记下了他们的学校和联系方式说也奇怪在山娃离城的头一天父亲居然请假陪山娃耍了一天那一天父亲陪着山娃辗转长隆水上乐园疯了一整天水上漂流高空冲浪看大马戏大凡里面有的父亲都带着他去疯一把山娃算了算这一次足足花了老爸元够他挣上半个月的山娃很不解一向节俭的父亲啥时变得如此阔绰大方大把大把掏钱时居然连眉头也不皱一下车票早买好了直达卧铺车得经子
一比一原版(QU毕业证)皇后大学毕业证成绩单
QU毕业证【微信95270640】办理皇后大学毕业证原版一模一样、QU毕业证制作【Q微信95270640】《皇后大学毕业证购买流程》《QU成绩单制作》皇后大学毕业证书QU毕业证文凭皇后大学
本科毕业证书,学历学位认证如何办理【留学国外学位学历认证、毕业证、成绩单、大学Offer、雅思托福代考、语言证书、学生卡、高仿教育部认证等一切高仿或者真实可查认证服务】代办国外（海外）英国、加拿大、美国、新西兰、澳大利亚、新西兰等国外各大学毕业证、文凭学历证书、成绩单、学历学位认证真实可查。
1:1完美还原海外各大学毕业材料上的工艺：水印阴影底纹钢印LOGO烫金烫银LOGO烫金烫银复合重叠。文字图案浮雕激光镭射紫外荧光温感复印防伪。
可办理以下真实皇后大学存档留学生信息存档认证：
1皇后大学真实留信网认证（网上可查永久存档无风险百分百成功入库）；
2真实教育部认证（留服）等一切高仿或者真实可查认证服务（暂时不可办理）；
3购买英美真实学籍（不用正常就读直接出学历）；
4英美一年硕士保毕业证项目（保录取学校挂名不用正常就读保毕业）
留学本科/硕士毕业证书成绩单制作流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询皇后大学皇后大学硕士毕业证成绩单）；
2开始安排制作皇后大学毕业证成绩单电子图；
3皇后大学毕业证成绩单电子版做好以后发送给您确认；
4皇后大学毕业证成绩单电子版您确认信息无误之后安排制作成品；
5皇后大学成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
— — — — — — — — — — — 《文凭顾问Q/微：95270640》很感动很无奈房东的儿子小伍笨手笨脚的不会说普通话满口粤语态度十分傲慢一副盛气凌人的样子山娃试图接近他跟他交友与城里人交友但他俩就好像是两个世界里的人根本拢不到一块儿不知不觉山娃倒跟周围出租屋里的几个小伙伴成了好朋友因为他们也是从乡下进城过暑假的小学生快乐的日子总是过得飞快山娃尚未完全认清那几位小朋友时他们却一个接一个地回家了山娃这时才恍然发现二个月的暑假已转到了尽头他的城市生活也将划上一个不很圆义
社内勉強会資料_LLM Agents .
単純な応答だけでなく、複雑な課題に対応できる柔軟性を持つLLM Agents。主要コンポーネントのPlanning、Memory、Tool Useについて解説しています。
一比一原版(ArtEZ毕业证)ArtEZ艺术学院毕业证成绩单
ArtEZ毕业证【微信95270640】☀《ArtEZ艺术学院毕业证购买》Q微信95270640《ArtEZ毕业证模板办理》文凭、本科、硕士、研究生学历都可以做,《文凭ArtEZ毕业证书原版制作ArtEZ成绩单》《仿制ArtEZ毕业证成绩单ArtEZ艺术学院学位证书pdf电子图》毕业证
[留学文凭学历认证(留信认证使馆认证)ArtEZ艺术学院毕业证成绩单毕业证证书大学Offer请假条成绩单语言证书国际回国人员证明高仿教育部认证申请学校等一切高仿或者真实可查认证服务。
多年留学服务公司,拥有海外样板无数能完美1:1还原海外各国大学degreeDiplomaTranscripts等毕业材料。海外大学毕业材料都有哪些工艺呢？工艺难度主要由：烫金.钢印.底纹.水印.防伪光标.热敏防伪等等组成。而且我们每天都在更新海外文凭的样板以求所有同学都能享受到完美的品质服务。
国外毕业证学位证成绩单办理方法：
1客户提供办理ArtEZ艺术学院ArtEZ艺术学院毕业证假文凭信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
— — — — 我们是挂科和未毕业同学们的福音我们是实体公司精益求精的工艺！ — — — -
一真实留信认证的作用(私企外企荣誉的见证):
1：该专业认证可证明留学生真实留学身份同时对留学生所学专业等级给予评定。
2：国家专业人才认证中心颁发入库证书这个入网证书并且可以归档到地方。
3：凡是获得留信网入网的信息将会逐步更新到个人身份内将在公安部网内查询个人身份证信息后同步读取人才网入库信息。
4：个人职称评审加20分个人信誉贷款加10分。
5：在国家人才网主办的全国网络招聘大会中纳入资料供国家500强等高端企业选择人才。却怎么也笑不出来山娃很迷惑父亲的家除了一扇小铁门连窗户也没有墓穴一般阴森森有些骇人父亲的城也便成了山娃的城父亲的家也便成了山娃的家父亲让山娃呆在屋里做作业看电视最多只能在门口透透气不能跟陌生人搭腔更不能乱跑一怕迷路二怕拐子拐人山娃很惊惧去年村里的田鸡就因为跟父亲进城一不小心被人拐跑了至今不见踪影害得田鸡娘天天哭得死去活来疯了一般那情那景无不令人摧肝裂肺山娃很听话天天呆在小屋里除了看书写作业就是睡带
一比一原版(IIT毕业证)伊利诺伊理工大学毕业证成绩单
IIT毕业证【微信95270640】购买（伊利诺伊理工大学毕业证成绩单硕士学历）Q微信95270640代办IIT学历认证留信网伪造伊利诺伊理工大学学位证书精仿伊利诺伊理工大学本科/硕士文凭证书补办伊利诺伊理工大学 diplomaoffer,Transcript购买伊利诺伊理工大学毕业证成绩单购买IIT假毕业证学位证书购买伪造伊利诺伊理工大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
#一整套伊利诺伊理工大学文凭证件办理#—包含伊利诺伊理工大学伊利诺伊理工大学毕业证假文凭学历认证|使馆认证|归国人员证明|教育部认证|留信网认证永远存档教育部学历学位认证查询办理国外文凭国外学历学位认证#我们提供全套办理服务。
一整套留学文凭证件服务：
一：伊利诺伊理工大学伊利诺伊理工大学毕业证假文凭毕业证 #成绩单等全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站永久可查
四：留信认证留学生信息网站永久可查
国外毕业证学位证成绩单办理方法：
1客户提供办理伊利诺伊理工大学伊利诺伊理工大学毕业证假文凭信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
教育部文凭学历认证认证的用途：
如果您计划在国内发展那么办理国内教育部认证是必不可少的。事业性用人单位如银行国企公务员在您应聘时都会需要您提供这个认证。其他私营 #外企企业无需提供！办理教育部认证所需资料众多且烦琐所有材料您都必须提供原件我们凭借丰富的经验帮您快速整合材料让您少走弯路。
实体公司专业为您服务如有需要请联系我: 微信95270640声和哐咣的关门声待山娃醒来时父亲早已上班去了床头总搁着山娃最爱吃的馒头和肉包还有白花花的豆浆父亲中午留在工地吃饭和午休山娃的中饭是对面快餐店送来的不用山娃付钱父亲早跟老板谈妥了钱到时一起结父亲给山娃配了台手机二手货诺基亚的父亲说有什么事只管给他挂电话能拥有自己的手机山娃很高兴除了玩游戏发短信除了挂电话给爷爷奶奶和母亲山娃还给班主任邱老师连挂了二个电话并给同学阿强和阿昌家挂山娃兴奋地向他们诉说城市一
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
CBU毕业证【微信95270640】《如何办理不列颠海角大学毕业证认证》【办证Q微信95270640】《不列颠海角大学文凭毕业证制作》《CBU学历学位证书哪里买》办理不列颠海角大学学位证书扫描件、办理不列颠海角大学雅思证书！
国际留学归国服务中心《如何办不列颠海角大学毕业证认证》《CBU学位证书扫描件哪里买》实体公司，注册经营，行业标杆，精益求精！
1:1完美还原海外各大学毕业材料上的工艺：水印阴影底纹钢印LOGO烫金烫银LOGO烫金烫银复合重叠。文字图案浮雕激光镭射紫外荧光温感复印防伪。
可办理以下真实不列颠海角大学存档留学生信息存档认证：
1不列颠海角大学真实留信网认证（网上可查永久存档无风险百分百成功入库）；
2真实教育部认证（留服）等一切高仿或者真实可查认证服务（暂时不可办理）；
3购买英美真实学籍（不用正常就读直接出学历）；
4英美一年硕士保毕业证项目（保录取学校挂名不用正常就读保毕业）
留学本科/硕士毕业证书成绩单制作流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询不列颠海角大学不列颠海角大学本科学位证成绩单）；
2开始安排制作不列颠海角大学毕业证成绩单电子图；
3不列颠海角大学毕业证成绩单电子版做好以后发送给您确认；
4不列颠海角大学毕业证成绩单电子版您确认信息无误之后安排制作成品；
5不列颠海角大学成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
— — — — — — — — — — — 《文凭顾问Q/微：95270640》这么大这么美的地方赚大钱高楼大厦鳞次栉比大街小巷人潮涌动山娃一路张望一路惊叹他发现城里的桥居然层层叠叠扭来扭去桥下没水却有着水一般的车水马龙山娃惊诧于城里的公交车那么大那么美不用买票乖乖地掷下二枚硬币空调享受还能坐着看电视呢屡经辗转山娃终于跟着父亲到家了山娃没想到父亲城里的家会如此寒碜更没料到父亲的城里竟有如此简陋的鬼地方父亲的家在高楼最底屋最下面很矮很黑是很不显眼的地下室父亲的家安在别人脚底下孰
The affect of service quality and online reviews on customer loyalty in the E...
Journal of Management and Creative Business.published book./
SOCRadar Germany 2024 Threat Landscape Report
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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The Modern Data Team for the Modern Data Stack: dbt and the Role of the Analytics Engineer
- 1. The modern data team for the modern data stack: dbt & the role of the analytics engineer
- 2. Welcome Jeremy Cohen Associate Product Manager he/him jeremy@fishtownanalytics.com @jerco (community.getdbt.com)
- 3. The modern data stack
- 4. The modern data team ▪ Custom ingestion ▪ Orchestration ▪ ML endpoints ▪ Platform, architecture, tooling: inform build vs. buy ▪ Provide lean, transformed data ready for analysis ▪ SWE practices to analytics code ▪ Maintain data documentation Analytics EngineerData Engineer Data Analyst ▪ Deep insights & forecasting ▪ Close partnership with business users ▪ Build & guarantee critical reporting
- 5. What is dbt? A. A python program B. The heart of the modern data stack C. An analytics engineer’s best friend D. A community of top-class data professionals E. All of the above
- 6. What is dbt, actually? ▪ Define, test, document, and reuse complex data transformation logic—just by writing SQL (and a little bit of YAML). ▪ dbt infers a DAG of transformations and runs models in order. ▪ Auto-generated documentation site, built from the same code as your transformations. The power of a framework, not the limitations of a GUI.
- 8. Extending SQL with Jinja ▪ Loops ▪ Macros ▪ Packages A pythonic templating engine to write DRYer code and leverage open source innovations.
- 9. The dbt community, by the numbers ▪ 2800+ companies running dbt in production across 12+ databases ▪ 48 open source packages of reusable macros and models ▪ 23k views: our opinionated best practices for dbt project design ▪ 7k data professionals at the top of their game in dbt Slack
- 10. +
- 11. dbt + ▪ Open source plugin ▪ pip install dbt-spark ▪ Write business logic in SparkSQL ▪ Dynamically template repetitive SQL with Jinja ▪ Connect to any Spark cluster + dbt run
- 12. Analytics engineering meets Delta Lake ▪ Access all core dbt features when you materialize models as Delta tables ▪ Use merge to build incremental models + snapshot slowly changing dimensions ▪ optimize zorder with hooks, operations, macros... The power of a data lake, the flexibility of a modern data warehouse, the intuition of a common modeling framework.
- 13. Announcing: dbt Cloud + Databricks ▪ Hosted IDE ▪ Compile + run SQL in real time ▪ Straightforward git flow ▪ No installation hassle ▪ Configurable job scheduler ▪ Continuous integration ▪ Host data documentation ▪ Persist dbt artifacts DeployDevelop Now in closed beta
- 14. Demo
- 15. How to deploy dbt? ▪ SaaS: up & running in minutes ▪ Enterprise: Fishtown-managed VPC, client-managed VPC, airgapped on-prem, … ▪ You! dbt, the Spark plugin, the documentation site: it’s all open source and can be deployed using standard infrastructure. Build, buy, or balance
- 16. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions.