data warehouse


ELT with Amazon Redshift – An Overview

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If you've been in Data Engineering, or what we once referred to as Business Intelligence, for more than a few years you've probably spent time building an ETL process. With the advent of (relatively) cheap storage and processing power in data warehouses, the majority of bulk data processing today is designed as ELT instead. Though this post speaks specifically to Amazon Redshift, most of the content is relevant to other similar data warehouse architectures such as Azure SQL Data Warehouse, Snowflake and Google BigQuery. First, ETL stands for "Extract-Transform-Load", while ELT just switches to order to "Extract-Load-Transform". Both are approaches to batch data processing used to feed data to a data warehouse and make it useful to analysts and reporting tools.


Microsoft shows off hybrid cloud management and cloud analytics tools at Ignite

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Microsoft's Ignite event traditionally attracts more from the developer ranks, but the technologies on display are increasingly of relevance to CIOs developing cloud strategies today. At Ignite 2019 in Orlando last week, Microsoft unveiled a new approach to analytics and data warehousing, Azure Synapse Analytics, and a new way to run Azure data services in anyone's cloud, Azure Arc. Get the latest cloud computing insights by signing up for our newsletter. With Azure Synapse Analytics Microsoft takes its Azure SQL Data Warehouse and turns up the volume to handle petabytes of data in its cloud. Some of the features -- such as dynamic data masking and column- and row-level security to provide granular access control -- are already generally available, while others -- notably integrations with Apache Spark, Power BI and Azure Machine Learning -- are still in preview.


Manager, Data Integration (Data Warehouse ) - IoT BigData Jobs

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Cars.com is a leader in the automotive digital marketplace. Since 1997, we have built our B2B and B2C brand to preeminent status in the industry. While enjoying great stability, we continue to grow. Our workforce has more than doubled since 2006, and our revenue has increased more than 150% in that same time. Our highly engaged workforce enjoys our dedication to work/life balance, wellness and career growth as well as a rich set of employee programs.


Manager, Data Integration (Data Warehouse ) - IoT BigData Jobs

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Cars.com is a leader in the automotive digital marketplace. Since 1997, we have built our B2B and B2C brand to preeminent status in the industry. While enjoying great stability, we continue to grow. Our workforce has more than doubled since 2006, and our revenue has increased more than 150% in that same time. Our highly engaged workforce enjoys our dedication to work/life balance, wellness and career growth as well as a rich set of employee programs.


From the Ground Up: Designing Yourself into a Data Engineer - PROPRIUS

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With the sheer amount of data produced in the world increasing exponentially every day, many companies are on the lookout for talented data engineers who can help them organize all that data and make sense of it. If you are interested in designing analytical tools and streamlining the machine learning processes to increase the efficiency of a company's data analysis, the title "data engineer" may be just the right one for you. Read on to find helpful advice for nailing that interview and landing your new job as a data engineer. In some cases, scientists and engineers are only responsible for one small part of a larger whole. They devote time and effort to making sure that one aspect of a project goes smoothly, and they don't think or worry about the other parts of the project.


Everything a Data Scientist Should Know About Data Management - KDnuggets

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To be a real "full-stack" data scientist, or what many bloggers and employers call a "unicorn," you have to master every step of the data science process -- all the way from storing your data, to putting your finished product (typically a predictive model) in production. But the bulk of data science training focuses on machine/deep learning techniques; data management knowledge is often treated as an afterthought. Data science students usually learn modeling skills with processed and cleaned data in text files stored on their laptop, ignoring how the data sausage is made. Students often don't realize that in industry settings, getting the raw data from various sources to be ready for modeling is usually 80% of the work. And because enterprise projects usually involve a massive amount of data that their local machine is not equipped to handle, the entire modeling process often takes place in the cloud, with most of the applications and databases hosted on servers in data centers elsewhere. Even after the student landed a job as a data scientist, data management often becomes something that a separate data engineering team takes care of. As a result, too many data scientists know too little about data storage and infrastructure, often to the detriment of their ability to make the right decisions at their jobs. The goal of this article is to provide a roadmap of what a data scientist in 2019 should know about data management -- from types of databases, where and how data is stored and processed, to the current commercial options -- so the aspiring "unicorns" could dive deeper on their own, or at least learn enough to sound like one at interviews and cocktail parties.


Machine Learning with SQL

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Python (and soon JavaScript with TensorFlow.js) is a dominant language for Machine Learning. There is a way to build/run Machine Learning models in SQL. There could be a benefit to run model training close to the database, where data stays. With SQL we can leverage strong data analysis out of the box and run algorithms without fetching data to the outside world (which could be an expensive operation in terms of performance, especially with large datasets). This post is to describe how to do Machine Learning in the database with SQL.


Welcome! You are invited to join a webinar: Production ML with the Autonomous Data Warehouse. After registering, you will receive a confirmation email about joining the webinar.

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We use data from a popular Kaggle competition, the Wisconsin Breast Cancer data, to build a binary classification model for the liklihood of a tumor being benign or malignant. We see how OAC's Data Visualization can be used to profile & explore the data, and can be used to do a rapid prototype of a Machine Learning model with DVML. See how ADW can be used to easily drop a Machine Learning model into production and enabled as a REST API for custom Applications and websites. By registering for this TechCast you give permission for your name and email address to be shared with the presenter and for BIWA User Community so we can inform you of future TechCasts and conferences of interest.


Introduction to Oracle Machine Learning - SQL Notebooks on top of Oracle Cloud Always Free Autonomous Data Warehouse - AMIS Oracle and Java Blog

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One of the relatively new features available with Oracle Autonomous Data Warehouse is Oracle Machine Learning Notebook. The description on Oracle's tutorial site states: "An Oracle Machine Learning notebook is a web-based interface for data analysis, data discovery, and data visualization." If you are familiar with Jupyter Notebooks (often Python based) then you may know and appreciate the Wiki like combination or markdown text and code snippets that are ideal for data lab'explorations' of data sets and machine learning models. I am quite a fan myself. Especially wrangling data, juggling with Pandas Data Frames and visualizing data with Plotly is good fun and it is quite easy to accomplish meaningful and advanced results.


Serverless Machine Learning Inference with Tika and TensorFlow

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The "strong applicant" prediction is provided by the DeepMatch API which uses features from your resume and the job. These features are computationally expensive to generate at scale and so need to be computed in advance, written to a serving store, and then combined for prediction when you visit. This post will guide you through how we built an event-driven serverless version of this architecture. It's aimed at data scientists, engineers, or anyone building ML products in production. Let's now talk more about these pieces in detail.