Cloudera

JUNE 18, 2022

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. On data warehouses and data lakes.

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Data Lake Data Warehouse BI SQL 92

Ascend.io

AUGUST 31, 2023

Secondly , the rise of data lakes that catalyzed the transition from ELT to ELT and paved the way for niche paradigms such as Reverse ETL and Zero-ETL. Still, these methods have been overshadowed by EtLT — the predominant approach reshaping today’s data landscape.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

Monte Carlo

JUNE 28, 2022

To help organizations realize the full potential of their data lake and lakehouse investments, Monte Carlo, the data observability leader, is proud to announce integrations with Delta Lake and Databricks’ Unity Catalog for full data observability coverage. billion in 2020 to 17.60 billion in 2020 to 17.60

Data Lake 52

Data Lake Metadata AWS Data Warehouse 52

Data Engineering Podcast

NOVEMBER 20, 2021

In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Start trusting your data with Monte Carlo today! How has that shifted the way that engineers approach pipeline design and composition?

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Data Warehouse Data Lake BI Business Intelligence 100

Hepta Analytics

FEBRUARY 14, 2022

We already had a script that downloaded a csv file, processed the data and pushed the data to postgres database. This week, we got to think about our data ingestion design. It is also preferred when the use case has more diverse business intelligence. The purpose of the data is not determined.

Data Ingestion 52

Data Ingestion Data Engineering Data Engineer Engineering 52

DataKitchen

AUGUST 3, 2021

The data mesh design pattern breaks giant, monolithic enterprise data architectures into subsystems or domains, each managed by a dedicated team. But first, let’s define the data mesh design pattern. The past decades of enterprise data platform architectures can be summarized in 69 words.

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Pharmaceutical Data Lake Data Architecture Scala 98

Monte Carlo

MARCH 3, 2021

Our SOC 2 designation means that Monte Carlo has designed a set of internal controls, systems, policies, and procedures that meet industry best practices for protecting our customer data. Specifically, with Monte Carlo, data never leaves your environment — full stop. What does this mean for you?

Metadata 52

Metadata Data Lake Business Intelligence Certification 52

AltexSoft

FEBRUARY 11, 2023

In the article, we explore the role of a data architect, discuss the responsibilities and required skills, and share what kind of companies may need such a specialist. What is a data architect? Data architecture is the organization and design of how data is collected, transformed, integrated, stored, and used by a company.

Data Architect 87

Data Architect Certification Generalist Big Data 87

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. Big Data Large volumes of structured or unstructured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Snowflake

NOVEMBER 2, 2023

Over the years, the technology landscape for data management has given rise to various architecture patterns, each thoughtfully designed to cater to specific use cases and requirements. Customers that require a hybrid of these to support many different tools and languages have built a data lakehouse.

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Data Lake Data Warehouse Cloud Unstructured Data 91

Data Engineering Podcast

DECEMBER 29, 2022

Rehgan Avon co-founded AlignAI to help address this challenge through a more purposeful platform designed to collect and distribute the knowledge of how and why data is used in a business. Atlan is the metadata hub for your data ecosystem. Missing data? Struggling with broken pipelines? Stale dashboards?

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Management Metadata Business Intelligence Data Lake 130

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

DECEMBER 18, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan's active metadata capabilities. Missing data? Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams.

Metadata 130

Metadata Business Intelligence Data Lake BI 130

Data Engineering Podcast

DECEMBER 25, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan's active metadata capabilities. Missing data? Again, be prepared to have metadata challenges especially. Struggling with broken pipelines? Stale dashboards?

Building 100

Building Metadata Business Intelligence Data Lake 100

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. Missing data?

Data Process 100

Data Process Process Metadata Business Intelligence 100

Cloudera

FEBRUARY 8, 2024

It has been designed and developed as an open community standard to ensure compatibility across languages and implementations. It brings the reliability and simplicity of SQL tables to big data while enabling engines like Hive, Impala, Spark, Trino, Flink, and Presto to work with the same tables at the same time.

Metadata 89

Metadata Data Warehouse BI AWS 89

Precisely

JANUARY 25, 2024

To achieve that, IT leaders must understand the various methods and tools available to them, and how to design a manageable roadmap that balances costs, risks, and benefits. Mainframe Modernization Patterns Every organization’s IT landscape is unique, as are its business processes and requirements. Best Practice 2. Best Practice 3.

Metadata 69

Metadata Data Governance Relational Database Government 69

Monte Carlo

JUNE 26, 2023

Over the past decade, Databricks and Apache Spark™ not only revolutionized how organizations store and process their data, but they also expanded what’s possible for data teams by operationalizing data lakes at an unprecedented scale across nearly infinite use cases. billion in 2020 to $17.6

Data Lake 52

Data Lake Metadata Bytes Google Cloud 52

Cloudera

AUGUST 30, 2022

Data Lakehouse: Data lakehouses integrate and unify the capabilities of data warehouses and data lakes, aiming to support artificial intelligence, business intelligence, machine learning, and data engineering use cases on a single platform. Towards Data Science ). Forrester ).

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Data Architecture Architecture Data Lake NoSQL 77

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

Monte Carlo

JUNE 14, 2023

In this post, we will help you quickly level up your overall knowledge of data pipeline architecture by reviewing: Table of Contents What is data pipeline architecture? Why is data pipeline architecture important? What is data pipeline architecture? Zero ETL is a bit of a misnomer.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

AltexSoft

OCTOBER 30, 2021

Data scientists need to have precise domain knowledge to extract the most relevant features for a model to use. Feature engineering is one of the most important and time consuming parts of a data scientist’s job. Data scientists are well versed in algorithms and data-related problems to be able to make a solid choice.

Data Engineering 98

Data Engineering Data Engineer Engineering Machine Learning 98

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!

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IT Data Lake Data Warehouse Cloud Storage 93

AltexSoft

DECEMBER 23, 2022

Whether your goal is data analytics or machine learning , success relies on what data pipelines you build and how you do it. But even for experienced data engineers, designing a new data pipeline is a unique journey each time. Data engineering in 14 minutes. ELT allows them to work with the data directly.

Process 52

Process Building Raw Data Data Lake 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 19, 2022

In the last few decades, we’ve seen a lot of architectural approaches to building data pipelines , changing one another and promising better and easier ways of deriving insights from information. There have been relational databases, data warehouses, data lakes, and even a combination of the latter two. What data mesh IS.

Architecture 52

Architecture Data Lake Medical Datasets 52

DataKitchen

OCTOBER 11, 2021

The final step is designing a data solution and its implementation. DataOps Project Design and Implementation. I quickly designed the DataOps solution to orchestrate the nuggets, tools, and environments in a single pipeline. . Figure 2: Example data pipeline with DataOps automation. Monitoring Job Metadata.

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Engineering BI Business Analyst Metadata 62

AltexSoft

JULY 29, 2022

According to the study by the Business Application Research Center (BARC), Hadoop found intensive use as. a suitable technology to implement data lake architecture. Hadoop distributed file system or HDFS is a data storage technology designed to handle gigabytes to terabytes or even petabytes of data.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

AltexSoft

JUNE 26, 2023

Data collection is a methodical practice aimed at acquiring meaningful information to build a consistent and complete dataset for a specific business purpose — such as decision-making, answering research questions, or strategic planning. Structured data is modeled to be easily searchable and occupy minimal storage space.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

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 is a data warehousing platform that runs on the cloud. What Does Snowflake Do?

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

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

ProjectPro

AUGUST 24, 2021

In this project, you will explore the usage of Databricks Spark on Azure with Spark SQL and build this data pipeline. Upload it to Azure Data lake storage manually. Create a Data Factory pipeline to ingest files. Also, you get a chance to explore Azure Databricks, Data Factory, and Storage services.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

ProjectPro

NOVEMBER 30, 2021

ETL (Extract, Transform, and Load) Pipeline involves data extraction from multiple sources like transaction databases, APIs, or other business systems, transforming it, and loading it into a cloud-hosted database or a cloud data warehouse for deeper analytics and business intelligence.

Process 52

Process Data Pipeline Data Warehouse AWS 52

ProjectPro

JANUARY 31, 2023

Data Storage: The next step after data ingestion is to store it in HDFS or a NoSQL database such as HBase. HBase storage is ideal for random read/write operations, whereas HDFS is designed for sequential processes. Data Processing: This is the final step in deploying a big data model. No reliability exists.

Big Data 52

Big Data Hadoop AWS Relational Database 52

ProjectPro

OCTOBER 20, 2021

BERT (Bidirectional Encoder Representations from Transformers): BERT is designed to pre-train deep bidirectional representations. Having multiple data integration routes helps optimize the operational as well as analytical use of data. Data Security and Governance These vulnerabilities can make or break AI Systems at Scale.

Machine Learning 52

Machine Learning Algorithm Government Data Science 52

Monte Carlo

JANUARY 23, 2024

Coined by Michael Kaminsky , editor of Locally Optimistic , the analytics engineer is a role that straddles data engineering and data analytics, and applies an analytical, business-oriented approach to working with data. The data engineer becomes almost like the keeper of good data habits.

Data Engineering 98

Data Engineering Data Engineer Engineering Data 98

Monte Carlo

JANUARY 23, 2023

When it comes to the data community, there’s always a debate broiling about something— and right now “data mesh vs data lake” is right at the top of that list. In this post we compare and contrast the data mesh vs data lake to illustrate the benefits of each and help discover what’s right for your data platform.

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Data Lake Unstructured Data Business Intelligence Architecture 52