Team Data Science

JANUARY 8, 2021

Big Data has become the dominant innovation in all high-performing companies. Notable businesses today focus their decision-making capabilities on knowledge gained from the study of big data. Big Data is a collection of large data sets, particularly from new sources, providing an array of possibilities for those who want to work with data and are enthusiastic about unraveling trends in rows of new, unstructured data.

Data Engineering 130

Data Engineering Data Engineer Engineering Unstructured Data 130

Start Data Engineering

JANUARY 6, 2021

What is backfilling ? Setup Prerequisites Apache Airflow - Execution Day Backfill Conclusion Further Reading References What is backfilling ? Backfilling refers to any process that involves modifying or adding new data to existing records in a dataset. This is a common use case in data engineering. Some examples can be a change in some business logic may need to be applied to an already processed dataset.

SQL 130

SQL Datasets Data Engineering Data Engineer 130

Teradata

JANUARY 5, 2021

By leveraging data to create a 360 degree view of its citizenry, government agencies can create more optimal experiences & improve outcomes such as closing the tax gap or improving quality of care.

Government 132

Government IT Data 132

Cloudera

JANUARY 6, 2021

Introduction. Python is used extensively among Data Engineers and Data Scientists to solve all sorts of problems from ETL/ELT pipelines to building machine learning models. Apache HBase is an effective data storage system for many workflows but accessing this data specifically through Python can be a struggle. For data professionals that want to make use of data stored in HBase the recent upstream project “hbase-connectors” can be used with PySpark for basic operations.

Machine Learning 132

Machine Learning Data Science Database Building 132

Advertisement

Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

Database

Data Engineering Podcast

JANUARY 4, 2021

Summary As more organizations are gaining experience with data management and incorporating analytics into their decision making, their next move is to adopt machine learning. In order to make those efforts sustainable, the core capability they need is for data scientists and analysts to be able to build and deploy features in a self service manner.

Python 100

Python Data Lake Machine Learning Computer Science 100

Confluent

JANUARY 7, 2021

At Zendesk, Apache Kafka® is one of our foundational services for distributing events among different internal systems. We have pods, which can be thought of as isolated cloud environments where […].

Kafka 117

Kafka Cloud Systems 117

Cloudera

JANUARY 7, 2021

Text classification is a ubiquitous capability with a wealth of use cases. For example, recommendation systems rely on properly classifying text content such as news articles or product descriptions in order to provide users with the most relevant information. Classifying user-generated content allows for more nuanced sentiment analysis. And in the world of e-commerce, assigning product descriptions to the most fitting product category ensures quality control. .

Machine Learning 105

Machine Learning Algorithm Deep Learning Designing 105

Teradata

JANUARY 3, 2021

Digital payments generate 90% of financial institutions’ useful customer data. How can they exploit its value? Find out more.

IT 89

IT Data 89

Data Council

JANUARY 7, 2021

Orchest is an open-source tool for creating data science pipelines. Its core value proposition is to make it easy to combine notebooks and scripts with a visual pipeline editor (“build”); to make your notebooks executable (“run”); and to facilitate experiments (“discover”).

Data Science 52

Data Science Building IT Data Pipeline 52

DataKitchen

JANUARY 6, 2021

Savvy executives maximize the value of every budgeted dollar. Decisions to invest in new tools and methods must be backed up with a strong business case. As data professionals, we know the value and impact of DataOps: streamlining analytics workflows, reducing errors, and improving data operations transparency. Being able to quantify the value and impact helps leadership understand the return on past investments and supports alignment with future enterprise DataOps transformation initiatives.

Pharmaceutical 98

Pharmaceutical Consulting Utilities Programming 98

Speaker: Scott Sehlhorst

We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.

Certification

Cloudera

JANUARY 5, 2021

In my last two blogs ( Get to Know Your Retail Customer: Accelerating Customer Insight and Relevance, and Improving your Customer-Centric Merchandising with Location-based in-Store Merchandising ) we looked at the benefits to retail in building personalized interactions by accessing both structured and unstructured data from website clicks, email and SMS opens, in-store point sale systems and past purchased behaviors.

Retail 91

Retail Unstructured Data Big Data Machine Learning 91

Teradata

JANUARY 7, 2021

The problem for regulators & for banks alike is agreeing what good data looks like & how to share it to create a modern, flexible, shared data model. Read more.

Banking 59

Banking Data IT 59

Silectis

JANUARY 3, 2021

If you’re new to data engineering or are a practitioner of a related field, such as data science, or business intelligence, we thought it might be helpful to have a handy list of commonly used terms available for you to get up to speed. This data engineering glossary is by no means exhaustive, but should provide some foundational context and information.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

DataKitchen

JANUARY 5, 2021

Remote working has revealed the inconsistency and fragility of workflow processes in many data organizations. The data teams share a common objective; to create analytics for the (internal or external) customer. Execution of this mission requires the contribution of several groups: data center/IT, data engineering, data science, data visualization, and data governance.

Data Governance 59

Data Governance Government Metadata Data Science 59

Speaker: Timothy Chan, PhD., Head of Data Science

Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.

Data Science

DataKitchen

JANUARY 2, 2021

In Gartner’s recent report, Operational AI Requires Data Engineering, DataOps, and Data-AI Role Alignment , Robert Thanaraj and Erick Brethenoux recognize that “organizations are not familiar with the processes needed to scale and promote artificial intelligence models from the prototype to the production stages; resulting in uncoordinated production deployment attempts.”.

Data Engineering 52

Data Engineering Data Engineer Engineering Government 52