Workfall

SEPTEMBER 18, 2023

Pre-filter and pre-aggregate data at the source level to optimize the data pipeline’s efficiency. Adapt to Changing Data Schemas: Data sources aren’t static; they evolve. Account for potential changes in data schemas and structures. Download Docker Desktop from here as a prerequisite.

Data Pipeline 93

Data Pipeline Raw Data Data Schemas Healthcare 93

Rockset

OCTOBER 6, 2022

Consequently, we needed a data backend with the following characteristics: Scale With ~50 commits per working day (and thus at least 50 pull request updates per day) and each commit running over one million tests, you can imagine the storage/computation required to upload and process all our data. What did we use before Rockset?

AWS 52

AWS Data Schemas Accessible Accessibility 52

Monte Carlo

APRIL 20, 2023

Prioritize data reliability efforts Data teams that take a “boil the ocean” approach to data quality will be stretched too thin, ultimately failing in their task. For example, your ability to ingest data is virtually limitless, but your capacity to document it is not. No data catalogs. No data dictionaries.

Data Warehouse 52

Data Warehouse BI Government Data 52

Snowflake

FEBRUARY 27, 2023

. “In a company, the purpose of data is not to please the data teams, but rather to serve the business itself, which must be able to make use of it in a self-service manner.” What’s the SLA? How should incidents be handled, and by whom? .

Cloud 52

Cloud Retail Data Warehouse Data 52

Tweag

FEBRUARY 22, 2023

This doesn’t mean one should give everyone full access to the infrastructure. The interface shouldn’t require much infrastructure knowledge nor expose low-level options: this isn’t simply about granting people access to an AWS console. I’ve rather heard a call for simpler, safer, and better automated processes. isn’t sustainable.

Management 52

Management Programming Language Data Schemas Programming 52

Monte Carlo

JUNE 28, 2022

Monte Carlo can automatically monitor and alert for data schema, volume, freshness, and distribution anomalies within the data lake environment. Delta Lake The Delta Lake is an open source storage layer that sits on top of and imbues an existing data lake with additional features that make it more akin to a data warehouse.

Data Lake 52

Data Lake Metadata AWS Data Warehouse 52

Knowledge Hut

OCTOBER 4, 2023

csv) – They are simplified text fields with rows of data. Database SQL database Access database Oracle database IBM Netezza MySQL database Sybase database Power Platform Power BI dataset Dataflows 4. It will ingest the data through Power BI and leverage the complete power of machine learning for easy collaboration.

BI 52

BI Systems Raw Data Business Intelligence 52

Knowledge Hut

APRIL 23, 2024

The focus is on consolidating and integrating data from different sources into a central repository. Big Data platforms accept diverse data sources, including structured, unstructured, and semi-structured data. This includes data from social media, sensors, logs, documents, multimedia content, and more.

Data Warehouse 52

Data Warehouse Big Data Unstructured Data Hadoop 52

Monte Carlo

JUNE 26, 2023

After launching our partnership with Databricks last year, Monte Carlo has aggressively expanded our native Databricks and Apache Spark™ integrations to extend data observability into the Delta Lake and Unity Catalog, and in the process, drive even more value for Databricks customers. We’re bringing observability to the data lake.

Data Lake 52

Data Lake Metadata Bytes Google Cloud 52

Knowledge Hut

MARCH 22, 2024

Versatility: The versatile nature of MongoDB enables it to easily deal with a broad spectrum of data types , structured and unstructured, and therefore, it is perfect for modern applications that need flexible data schemas. Designing and implementing RESTful APIs for MongoDB data access.

MongoDB 52

MongoDB Amazon Web Services Computer Science Education 52

Monte Carlo

AUGUST 2, 2022

Here’s how teams on Databricks and Monte Carlo can benefit from our strategic partnership: Achieve end-to-end data observability across your Databricks Lakehouse Platform without writing code. Get full, automated coverage across your data pipelines with a low-code implementation process. Know when data breaks, as soon as it happens.

Building 52

Building Data Lake Business Intelligence Data Pipeline 52

Confluent

MAY 28, 2019

The primary cluster: Coordinates primary election among all the Schema Registry instances. Contains the schemas topic, to which primary instances back up newly registered schemas. Confluent Replicator then copies the Kafka schemas topic from the primary cluster to the other cluster for backup. powered by Typeform.

Management 83

Management Kafka Java Certification 83

Monte Carlo

JUNE 9, 2022

Forrester calculates data quality issues take up around 40% of a data professional’s time and recent Monte Carlo commissioned surveys have validated that finding. Data observability solutions capability to automate lineage can help in this regard. Don’t let syntax issues hold up your migration.

IT 52

IT Healthcare Raw Data Data Warehouse 52

Monte Carlo

FEBRUARY 6, 2023

Migrations require support from everyone from data engineers and stakeholders to cross-functional partners in order to be successful, so it’s critically important to get the right people around the table early. What teams will be using your new data warehouse? What will they need access to and when? Is your data structured?

Data Warehouse 52

Data Warehouse AWS Data Validation Data 52

Confluent

JUNE 13, 2019

The data from these detections are then serialized into Avro binary format. The Avro alert data schemas for ZTF are defined in JSON documents and are published to GitHub for scientists to use when deserializing data upon receipt.

Kafka 101

Kafka Bytes Data Pipeline Python 101

Cloud Academy

JANUARY 27, 2022

They need to understand common data formats and interfaces, and the pros and cons of different storage options. Data engineers are responsible for transforming data into an easily accessible format, identifying trends in data sets, and creating algorithms to make the raw data more useful for business units.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

U-Next

SEPTEMBER 22, 2022

These measures will ensure the business is protected against any kinds of data breaches, malware attacks, and unauthorized entry. In addition to upgrading the existing security systems, testing security products, and overseeing the preparation of technical documentation, disaster recovery plans, etc.,

Consulting 40

Consulting Database Programming Computer Science 40

Netflix Tech

OCTOBER 23, 2019

This combination of code, data and execution results into a single document makes notebooks powerful, but also difficult to reproduce. Indeed, the scientific computing community has documented some notebook reproducibility concerns as well as some best practices for reproducible notebooks.

Scala 93

Scala Machine Learning Python Coding 93

Rockset

DECEMBER 9, 2021

Deeply-nested JSON and dynamic schemas. Real-time data streams typically arrive raw and semi-structured, say in the form of a JSON document, with many levels of nesting. Moreover, new fields and columns of data are constantly appearing. These can easily break rigid data pipelines in the batch world.

Transportation 52

Transportation BI SQL Data Warehouse 52

Monte Carlo

APRIL 24, 2023

If streaming data is a priority for your platform, you might also choose to leverage a system like Confluent’s Apache Kafka along with some of the above mentioned technologies. dbt Core is a free open source solution that gives team’s access to the dbt platform using command prompts. When should you use dbt Core?

Data Pipeline 52

Data Pipeline Building Data Ingestion BI 52

Ascend.io

MAY 19, 2023

Stage 3 begins as these early adopters collaborate formally and informally, identifying and documenting best practices and patterns in the form of “reference architectures”. In our case, data ingestion, transformation, orchestration, reverse ETL, and observability. Sounds familiar? Second, it enhances governance and security.

Data Pipeline 52

Data Pipeline Data Engineering Data Engineer Media 52

Netflix Tech

OCTOBER 23, 2019

This combination of code, data and execution results into a single document makes notebooks powerful, but also difficult to reproduce. Indeed, the scientific computing community has documented some notebook reproducibility concerns as well as some best practices for reproducible notebooks.

Scala 43

Scala Machine Learning Python Coding 43

Netflix Tech

OCTOBER 23, 2019

This combination of code, data and execution results into a single document makes notebooks powerful, but also difficult to reproduce. Indeed, the scientific computing community has documented some notebook reproducibility concerns as well as some best practices for reproducible notebooks.

Scala 40

Scala Machine Learning Python Coding 40

ProjectPro

AUGUST 21, 2015

Hadoop vs RDBMS Criteria Hadoop RDBMS Datatypes Processes semi-structured and unstructured data. Processes structured data. Schema Schema on Read Schema on Write Best Fit for Applications Data discovery and Massive Storage/Processing of Unstructured data. are all examples of unstructured data.

Hadoop 40

Hadoop Big Data Metadata Java 40