Monte Carlo

FEBRUARY 21, 2023

In this post, we’ll discuss what, exactly, a data fabric is, how other companies have used it, and how you can build one at your company. Table of Contents What is a data fabric? As your team builds your data fabric, make sure you have a designated way to collect the various metadata associated with your data inputs.

Data Architecture 52

Data Architecture Architecture Metadata Unstructured Data 52

Data Engineering Podcast

MARCH 22, 2021

Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows.

Data Warehouse 100

Data Warehouse Metadata Data Lake Hadoop 100

AltexSoft

FEBRUARY 11, 2023

What’s more, investing in data products, as well as in AI and machine learning was clearly indicated as a priority. This suggests that today, there are many companies that face the need to make their data easily accessible, cleaned up, and regularly updated.

Data Architect 87

Data Architect Certification Generalist Big Data 87

Hepta Analytics

FEBRUARY 14, 2022

This week, we got to think about our data ingestion design. We looked at the following: How do we ingest – ETL vs ELT Where do we store the data – Data lake vs data warehouse Which tool to we use to ingest – cronjob vs workflow engine NOTE : This weeks task requires good internet speed and good compute.

Data Ingestion 52

Data Ingestion Data Engineering Data Engineer Engineering 52

Silectis

JANUARY 3, 2021

This data engineering glossary is by no means exhaustive, but should provide some foundational context and information. Advanced Analytics The process of discovering deeper insights in data than typically enabled by most business intelligence (BI) tools. Big Data Large volumes of structured or unstructured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

ProjectPro

FEBRUARY 16, 2023

Security AWS offers various security features companies can use to protect their data, such as encryption, access controls, and network isolation. This ensures that companies' data is always protected and secure. AWS Glue has a central metadata repository called the Glue catalog.

AWS 52

AWS Data Management ETL Tools Management 52

Monte Carlo

AUGUST 25, 2023

That’s why it’s essential for teams to choose the right architecture for the storage layer of their data stack. But, the options for data storage are evolving quickly. Different vendors offering data warehouses, data lakes, and now data lakehouses all offer their own distinct advantages and disadvantages for data teams to consider.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Data Engineering Podcast

NOVEMBER 26, 2023

Summary Building a data platform that is enjoyable and accessible for all of its end users is a substantial challenge. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. Data lakes are notoriously complex.

Architecture 147

Architecture Data Lake High Quality Data SQL 147

Monte Carlo

MAY 31, 2022

Metadata from the data warehouse/lake and from the BI tool of record can then be used to map the dependencies between the tables and dashboards. Integrating with it is the holy grail of Spark lineage because it contains all the information needed for how data moves through the data lake and how everything is connected.

Data Lake 52

Data Lake Building Scala Metadata 52

Data Engineering Podcast

NOVEMBER 27, 2022

Summary The data ecosystem has been growing rapidly, with new communities joining and bringing their preferred programming languages to the mix. This has led to inefficiencies in how data is stored, accessed, and shared across process and system boundaries. Atlan is the metadata hub for your data ecosystem.

Data Process 100

Data Process Process Metadata Business Intelligence 100

Cloudera

JANUARY 21, 2021

With CDW, as an integrated service of CDP, your line of business gets immediate resources needed for faster application launches and expedited data access, all while protecting the company’s multi-year investment in centralized data management, security, and governance. Proprietary file formats mean no one else is invited in!

IT 93

IT Data Lake Data Warehouse Cloud Storage 93

Cloudera

AUGUST 30, 2022

Data is the fuel that drives government, enables transparency, and powers citizen services. For state and local agencies, data silos create compounding problems: Inaccessible or hard-to-access data creates barriers to data-driven decision making. A simple example from a recent article in StateTech makes this case.

Data Architecture 76

Data Architecture Architecture Data Lake NoSQL 76

Cloudera

MARCH 23, 2022

Apache Iceberg is a high-performance, open table format, born-in-the cloud that scales to petabytes independent of the underlying storage layer and the access engine layer. By being a truly open table format, Apache Iceberg fits well within the vision of the Cloudera Data Platform (CDP). What is Apache Iceberg? 2: Open formats.

Metadata 88

Metadata Data Architecture BI Machine Learning 88

Monte Carlo

JUNE 14, 2023

This frequently involves, in some order, extraction (from a source system), transformation (where data is combined with other data and put into the desired format), and loading (into storage where it can be accessed). In many cases the data stays in a data lake and is queried from there versus moving to the data warehouse.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

AltexSoft

NOVEMBER 10, 2021

The pun being obvious, there’s more to that than just a new term: Data lakehouses combine the best features of both data lakes and data warehouses and this post will explain this all. What is a data lakehouse? Data warehouse vs data lake vs data lakehouse: What’s the difference.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

Cloudera

JUNE 30, 2022

With Cloudera’s vision of hybrid data , enterprises adopting an open data lakehouse can easily get application interoperability and portability to and from on premises environments and any public cloud without worrying about data scaling. Why integrate Apache Iceberg with Cloudera Data Platform?

Data Lake 84

Data Lake Business Intelligence Metadata Data Warehouse 84

AltexSoft

OCTOBER 30, 2021

Here, data scientists are supported by data engineers. Data engineering itself is a process of creating mechanisms for accessing data. Managing data and metadata. There are different ways how data can be stored: a data warehouse, numerous data lakes and data hubs , etc.

Data Engineering 98

Data Engineering Data Engineer Engineering Machine Learning 98

LinkedIn Engineering

JULY 27, 2023

It’s now much easier to manage large infra requirements that have traditionally demanded an amalgamation of teams like DBA, Infra-SRE, Onprem-SMEs, network managers, and access control managers working together. Data connections are secured through Azure Key Vaults and network connectivity is protected by LinkedIn's NACL control.

Metadata 75

Metadata Utilities Cloud Data Lake 75

Towards Data Science

JULY 24, 2023

Why are these things related, and more importantly, why should data leaders care? Well, the connective tissue that powers and integrates these services is frequently found in the metadata of table formats (storage) and query/job logs (compute). Take data governance for example.

Metadata 97

Metadata Data Warehouse Data Lake Data Science 97

Cloudera

SEPTEMBER 9, 2022

The promise of a modern data lakehouse architecture. Imagine having self-service access to all business data, anywhere it may be, and being able to explore it all at once. Imagine quickly answering burning business questions nearly instantly, without waiting for data to be found, shared, and ingested.

Architecture 97

Architecture Metadata Unstructured Data Machine Learning 97

AltexSoft

NOVEMBER 23, 2021

Not to mention that additional sources are constantly being added through new initiatives like big data analytics , cloud-first, and legacy app modernization. To break data silos and speed up access to all enterprise information, organizations can opt for an advanced data integration technique known as data virtualization.

Process 69

Process Data Lake Metadata Data Warehouse 69

Monte Carlo

DECEMBER 14, 2022

Lineage graphs are automatically created by Monte Carlo using metadata from Unity Catalog to provide an overview of your object relations as well as incidents associated with each object, for the purpose of accelerating your incident resolution timelines. The dashboard also indicates overall data quality as well as areas of opportunity.

Data Lake 52

Data Lake BI Business Intelligence Metadata 52

AltexSoft

SEPTEMBER 23, 2021

One of the innovative ways to address this problem is to build a data hub — a platform that unites all your information sources under a single umbrella. This article explains the main concepts of a data hub, its architecture, and how it differs from data warehouses and data lakes. What is Data Hub?

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

AltexSoft

JULY 29, 2022

a runtime environment (sandbox) for classic business intelligence (BI), advanced analysis of large volumes of data, predictive maintenance , and data discovery and exploration; a store for raw data; a tool for large-scale data integration ; and. a suitable technology to implement data lake architecture.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

Monte Carlo

JANUARY 25, 2023

When it comes to the question of building or buying your data stack, there’s never a one-size-fits-all solution for every data team—or every component of your data stack. The three most popular solutions for data storage and compute include the data warehouse, data lake, and data lakehouse.

Data Warehouse 40

Data Warehouse Building Data Lake Data Storage 40

AltexSoft

NOVEMBER 7, 2022

Metadata database. A metadata database stores information about user permissions, past and current DAG and task runs, DAG configurations, and more. By default, Airflow handles metadata with SQLite which is meant for development only. Full REST API: easy access for third parties. Since the 2.0

PostgreSQL 52

PostgreSQL Metadata Python MySQL 52

Monte Carlo

SEPTEMBER 23, 2021

Data lineage provides the answer by telling you which upstream sources and downstream ingestors were impacted, as well as which teams are generating the data and who is accessing it. Full-stack coverage from ingestion to the BI layer Full-stack coverage is necessary to fully understand data health.

BI 52

BI Data Metadata Data Pipeline 52

Monte Carlo

AUGUST 10, 2023

Data lineage provides the answer by telling you which upstream sources and downstream ingestors were impacted, as well as which teams are generating the data and who is accessing it. A small change to your organization’s codebase that causes an API to stop collecting data that powers a critical field in your Tableau dashboard.

Data Pipeline 98

Data Pipeline Software Engineer Software Engineering Machine Learning 98

Monte Carlo

AUGUST 16, 2022

With Monte Carlo, data engineering teams can proactively manage the health of their data across the entire data stack — including data warehouses, data lakes, BI tools, data orchestration tools, and of course dbt — on a single platform.

Engineering 52

Engineering Data Lake Retail BI 52

AltexSoft

JUNE 26, 2023

Commonly, the entire flow is fully automated and consists of three main steps — data extraction, transformation, and loading ( ETL or ELT , for short, depending on the order of the operations.) Dive deeper into the subject by reading our article Data Integration: Approaches, Techniques, Tools, and Best Practices for Implementation.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Advancing Analytics: Data Engineering

NOVEMBER 10, 2020

We have discussed in the past this idea of the lakehouse , the aspirational target of many analytics platforms these days of combining the huge power and potential of data lakes with the rigour, reliability and concurrency of a data warehouse. The Modern Data Warehouse. The Lakehouse Architecture.

SQL 52

SQL BI Data Warehouse Data Lake 52

ProjectPro

FEBRUARY 8, 2017

The need for speed to use Hadoop for sentiment analysis and machine learning has fuelled the growth of hadoop based data stores like Kudu and adoption of faster databases like MemSQL and Exasol. In 2017, big data platforms that are just built only for hadoop will fail to continue and the ones that are data and source agnostic will survive.

Big Data 40

Big Data Hadoop Data Lake Data Governance 40

Snowflake

MARCH 30, 2023

Based on Tecton blog So is this similar to data engineering pipelines into a data lake/warehouse? Let’s explore that last point a little more closely… Data versioning Raw vault stores the business process output in its applied state as raw hub, link, and satellite tables.

Engineering 82

Engineering Raw Data Data Science Scala 82

ProjectPro

JANUARY 31, 2022

This is the reason why we need Data Warehouses. What is Snowflake Data Warehouse? A Data Warehouse is a central information repository that enables Data Analytics and Business Intelligence (BI) activities. Snowflake Data Marketplace gives users rapid access to various third-party data sources.

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

ProjectPro

DECEMBER 10, 2021

Azure Cloud Services are hosted on virtual machines, and they let one have a higher degree of control over the VMs by allowing software installation on the VMs and also making them remotely accessible. A user only pays for the resources he needs from a cloud service provider and accesses them over a secure Internet connection.

BI 52

BI Cloud Computing SQL Database 52

Cloudera

AUGUST 21, 2020

And next to those legacy ERP, HCM, SCM and CRM systems, that mysterious elephant in the room – that “Big Data” platform running in the data center that is driving much of the company’s analytics and BI – looks like a great potential candidate. . They facilitate access to what has been developed.

Hadoop 73

Hadoop Big Data Cloud Kafka 73

AltexSoft

OCTOBER 8, 2021

They are applied to retrieve data from the source systems, perform transformations when necessary, and load it into a target system ( data mart , data warehouse, or data lake). So, why is data integration such a big deal? Connections to both data warehouses and data lakes are possible in any case.

Data Integration 52

Data Integration Hadoop Data Warehouse Data Lake 52