Cloudera

SEPTEMBER 28, 2020

The data lifecycle model ingests data using Kafka, enriches that data with Spark-based batch process, performs deep data analytics using Hive and Impala, and finally uses that data for data science using Cloudera Data Science Workbench to get deep insights. Hive, Ranger, Atlas, Spark. Hive, Ranger, Atlas, Spark. Convert Spark 1.x

Cloud 131

Cloud Kafka Professional Services Metadata 131

Snowflake

DECEMBER 19, 2023

Every customer store interaction, online transaction, form fill, event participation, chatbot response, text request, like, review, complaint, and click creates another fragment of data tagged by a variety of identifier “keys.” And their customers liked what they saw, according to Erin Boelkens, VP of Product, Identity at LiveRamp.

Building 80

Building Pipeline-centric Database-centric Digital Media 80

Knowledge Hut

DECEMBER 28, 2023

There is also Apache OpenNLP, which is a toolkit for natural language processing that includes features like text tokenization, part-of-speech tagging, and named entity identification. The Cyberitis project's Big Data components incorporate Hadoop, Spark, and Storm tools to enable outlier and anomaly detection.

Hadoop 52

Hadoop Project Datasets Big Data 52

Cloudera

FEBRUARY 8, 2023

How Bad is Bad Code: The ROI of Fixing Broken Spark Code Once in a while I stumble upon Spark code that looks like it has been written by a Java developer and it never fails to make me wince because it is a missed opportunity to write elegant and efficient code: it is verbose, difficult to read, and full of distributed processing anti-patterns.

Java 57

Java Datasets Coding Python 57

Lyft Engineering

OCTOBER 3, 2023

Metadata  —  This includes a human-readable name, a universally unique identifier (UUID), ownership information, and tags (arbitrary semantic aggregations like ‘ML-feature’ or ‘business-reporting’). This has useful aggregations like owning team, table name, and tags. It can be a fixed threshold or a statistical one.

Big Data 84

Big Data Metadata Data Warehouse Data 84

Advancing Analytics: Data Engineering

OCTOBER 11, 2021

It translates operations into optimized logical and physical plans and shows what operations are going to be executed and sent to the Spark Executors. Catalyst optimizer flow: The execution process is as follows: If the code written is valid then Spark converts this into a Logical Plan. Execution Flow.

Metadata 52

Metadata SQL Python Coding 52

Cloudera

APRIL 30, 2021

Apache Spark is now widely used in many enterprises for building high-performance ETL and Machine Learning pipelines. If the users are already familiar with Python then PySpark provides a python API for using Apache Spark. Apache Spark provides several options to manage these dependencies.

Python 62

Python Data Engineering Data Engineer Engineering 62

Data Engineering Podcast

OCTOBER 12, 2020

Equalum also leverages open source data frameworks by orchestrating Apache Spark, Kafka and others under the hood. Tool consolidation and linear scalability without the legacy platform price tag. Equalum also leverages open source data frameworks by orchestrating Apache Spark, Kafka and others under the hood.

Business Intelligence 100

Business Intelligence BI Consulting Data Ingestion 100

Cloudera

AUGUST 18, 2021

It makes more sense to analyze and derive insights from it, and then place it in the data lake — properly tagged for easy access later. If the data goes into a data lake before analysis, extracting it can get pretty complex and time-consuming. Data source diversity also must be addressed because it, too, adds complexity.

Medical 87

Medical Hospitality Data Lake Healthcare 87

Cloudera

DECEMBER 21, 2020

The data is tagged as sensitive data, e.g. “financial”, and the owner field showing “retail banking” instantly informs Shaun which organization to reach out to to ask for access. For each table, she first views the lineage, to understand which source data is entailed and takes a quick look at the classifications and tags. .

Banking 97

Banking Data Lake Data Data Warehouse 97

Towards Data Science

APRIL 6, 2023

Create Python or Spark processing jobs using the visual interface, code editor, or Jupyter notebooks. I choose to use Spark because I’m more familiar with it. If you are interested in understanding a little more about Spark, check out one of my previous posts. But, instead of GCP, we’ll be using AWS. S3 is AWS’ blob storage.

AWS 79

AWS Data Pipeline Amazon Web Services Python 79

Cloudera

JUNE 15, 2021

You can include in this section services such as Apache Spark 3 , Apache NiFi or Apache Flink although these will require configuration of separate CSD s. We can run the playbook in stages using some specific tags , or just run the whole thing end to end. <Comma separated list of tags> To run the playbook in increments.

Cloud 83

Cloud AWS Kafka Management 83

Knowledge Hut

JUNE 20, 2023

Apache Spark, Microsoft Azure, Amazon Web services, etc. The top five tools are mentioned below: Apache Spark: An open-source data analytics engine that notable firms like Apple, Microsoft, and IBM use. Spark is an efficient solution for big data engineering.

Machine Learning 52

Machine Learning Data Engineering Data Engineer Engineering 52

Knowledge Hut

MARCH 13, 2024

To ensure the datasets are correctly handled, the Big Data Engineer should be thorough with various ETL tools, SQL tools, frameworks like Hadoop and Apache Spark, and programming languages like Python or Java. Thus, the role demands prior experience in handling large volumes of data.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

Knowledge Hut

MARCH 13, 2024

To ensure the datasets are correctly handled, the Big Data Engineer should be thorough with various ETL tools, SQL tools, frameworks like Hadoop and Apache Spark, and programming languages like Python or Java. Thus, the role demands prior experience in handling large volumes of data.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

Knowledge Hut

SEPTEMBER 29, 2023

Determine the role of tags in cost allocation. Benefits of AWS Cloud Practitioner Certification AWS certification has sparked a wave of transformation among applicants all over the world. Recognize the different account structures as they relate to AWS billing and pricing. Determine the billing support resources that are available.

AWS 52

AWS Cloud Cloud Computing Amazon Web Services 52

Cloudera

FEBRUARY 11, 2021

Resource-based access control (RBAC) policies can be set up for Kudu in Ranger, but Kudu currently doesn’t support tag-based policies, row-level filtering or column masking. Let’s take a common use case as an example: several Apache Spark ETL jobs store data in Kudu.

Hadoop 52

Hadoop Metadata Java Database 52

Cloudera

JANUARY 30, 2019

With Cloudera Data Science Workbench, data scientists can: Use R, Python, or Scala along with the scale-out processing capabilities of Apache Spark 2.X Add it to an existing HDP cluster, and it just works. X on HDP clusters from a web browser, with no desktop footprint. Utilize GPUs effectively for workload specific needs.

Data Science 72

Data Science Machine Learning Scala Government 72

Knowledge Hut

SEPTEMBER 29, 2023

Additionally, Apache Spark can be used to learn ingestion methods. This AI engineer would ask a data engineer to set up an Azure Cosmos DB instance so that the computer vision application's generated tags and metadata could be stored there. Then, you can create analytical layer serving designs.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

ProjectPro

NOVEMBER 14, 2015

Apache Hadoop and Apache Spark fulfill this need as is quite evident from the various projects that these two frameworks are getting better at faster data storage and analysis. These Apache Spark projects are mostly into link prediction, cloud hosting, data analysis, and speech analysis. Why Apache Spark? Data Migration 2.

Hadoop 52

Hadoop Project Big Data Healthcare 52

ProjectPro

DECEMBER 21, 2021

A Machine Learning professional needs to have a solid grasp on at least one programming language such as Python, C/C++, R, Java, Spark, Hadoop, etc. There are numerous machine learning libraries/packages/APIs support machine learning algorithm implementations such as scikit-learn, Spark MLlib, H2O, TensorFlow, etc.

Machine Learning 52

Machine Learning Engineering Algorithm Computer Science 52

Monte Carlo

MARCH 12, 2024

For Airflow lineage, Monte Carlo relies on query tagging to ingest DAGs and tasks related to tables. This means leveraging functions like Snowflake query tags, BigQuery labels, query comments, cluster policies or dbt macros. We have continued to advance these capabilities in significant ways to help data teams improve data reliability.

BI 52

BI Metadata Data Pipeline Data Engineering 52

ProjectPro

APRIL 20, 2017

The collection of these projects on Hadoop and Spark will help professionals master the big data and Hadoop ecosystem concepts learnt during their hadoop training. MovieLens dataset consists of 22884377 ratings and 586994 tag applications across 34208 movies created by 247753 users. What will you learn from this Hadoop Project?

Hadoop 40

Hadoop Big Data Coding Project 40

AltexSoft

OCTOBER 25, 2021

Say, the system can tag data from patient history, discharge summary, or call center reports and then structure them in an EHR according to a schema. Named after the Victorian physician who used analytics to trace the cholera outbreak in 1854, the company offers Spark NLP — a library with 200+ pretrained models. John Snow Labs.

Medical 52

Medical Healthcare Process Hospitality 52

Netflix Tech

DECEMBER 3, 2022

job: id: ddl type: Spark spark: script: $S3{./ddl/dataflow_sparksql_sample.sql} See example below: - template: id: wap type: wap tables: - ${CATALOG}/${DATABASE}/${TABLE} write_jobs: - job: id: write type: Spark spark: script: $S3{./src/sparksql_write.sql} test_sparksql_write.py test_sparksql_write.py

Data Pipeline 143

Data Pipeline Scala Metadata Food 143

Ascend.io

MAY 24, 2023

Healthcare Data Pipeline Evolution: From SQL to Spark The SQL Era In the early days of our data journey, pipelines were crafted in many mySQL databases. Spark and Ascend: The Big Data Processing Solution Yet, as data volumes continued to swell, processing time still crept upwards.

Healthcare 52

Healthcare Data Pipeline Hospitality Datasets 52

LinkedIn Engineering

DECEMBER 13, 2023

There are also LinkedIn Learning courses where only a subset of skills are tagged directly, with a number of relevant skills being mentioned solely in the description or in the course itself (i.e., Skill tagging Once the unstructured raw input is properly parsed, a skill tagger identifies the mentions of skills in the text.

Recruitment 83

Recruitment Utilities Designing Java 83

Cloudera

OCTOBER 18, 2022

Waste identification: special dashboards follow patterns in our consumption and provide actionable intelligence, empowering the owners to spark conversations or directly reach out to the right team to make changes and eliminate waste. Object owners, which can be mapped back to organizational unit, and therefore cost center.

Cloud 85

Cloud IT Data Warehouse AWS 85

Towards Data Science

MARCH 9, 2023

When working with NLP applications it gets even deeper with stages like stemming, lemmatization, stop word removal, tokenization, vectorization, and part of speech tagging (POS tagging). link] Now that we’ve successfully created and applied the pipeline with scikit-learn let’s do the same with Apache Spark’s library MLLib.

Machine Learning 78

Machine Learning Building Datasets Scala 78

Monte Carlo

MAY 31, 2022

Field-level data lineage (not necessarily Spark lineage) with hundreds of connections between objects in upstream and downstream tables. for SQL based transformations, but some of the most popular, Spark-based systems remained a blindspot for us and for the industry at large. Easy compared to Spark lineage? Absolutely.

Data Lake 52

Data Lake Building Scala Metadata 52

ProjectPro

JANUARY 25, 2022

Did you know that, according to Linkedin, over 24,000 Big Data jobs in the US list Apache Spark as a required skill? Learning Spark has become more of a necessity to enter the Big Data industry. Apache Spark is one of the most popular frameworks for managing and dealing with Big Data. This is where Apache Spark PySpark comes in.

Big Data 52

Big Data Data Process Process Kafka 52

AltexSoft

JULY 18, 2023

On the other hand, the term spark often brings to mind a tiny particle that, despite its size, can start a large fire. These seemingly unrelated terms unite within the sphere of big data, representing a processing engine that is both enduring and powerfully effective — Apache Spark. What is Apache Spark? Apache Spark components.

Big Data 52

Big Data Data Process Process Hadoop 52

Snowflake

NOVEMBER 2, 2023

In addition, governors can take bulk action to propagate tags and policies to protect all downstream columns that have personally identifiable information. We have also contributed an SDK to the open source Apache Iceberg project, which allows Apache Spark clients to access metadata when reading Snowflake-managed Iceberg Tables.

Metadata 103

Metadata Government AWS Medical 103

Knowledge Hut

NOVEMBER 17, 2023

Amazon Elastic MapReduce (EMR) helps efficiently process and analyze big data using servers like Spark and Hadoop. Amazon EMR It is an AWS data science platform for easy execution and processing of big data frameworks, such as Apache, Hadoop and Spark. Apache Spark - a cluster framework for processing big data.

AWS 52

AWS Data Science Certification Amazon Web Services 52

ProjectPro

FEBRUARY 18, 2022

Intermediate ETL Project Ideas for Practice Oil Field Data Analytics using Spark, HBase, and Phoenix Using Apache Spark , HBase, and Apache Phoenix, create a Real-Time Streaming Data Pipeline for a system that analyzes oil wells. Using Spark, read data from HDFS storage and write it to an HBase database.

Project 52

Project AWS Kafka Healthcare 52

Airbnb Tech

APRIL 13, 2021

At the company scale, visibility into cost and usage has sparked a cultural shift. Apache Airflow , Apache Hive, Apache Spark ) and extensive analytics infrastructure (i.e., Project Name  — This is a user-defined tag which is surfaced in the CUR data. For example, the Viaduct project has its own tag.

AWS 52

AWS Raw Data Amazon Web Services Cloud 52

Zalando Engineering

JUNE 9, 2022

The ROI pipeline is a batch based data- and machine learning pipeline powered by Databricks Spark and orchestrated by Apache Airflow. As our data layer, we’re mainly using AWS S3 with data organized as Spark tables. A set of Spark tables represents a data environment. ')[ 1 ]} tags. zip/qu/main/file.py feature_name.{

Database 52

Database Coding Python AWS 52