OLAP and Hadoop: The 4 Differences You Should Know

Perficient Data & Analytics

OLAP and Hadoop are not the same. OLAP is a technology to perform multi-dimensional analytics like reporting and data mining. They can be used together but there are differences when choosing between using Hadoop/MapReduce data processing versus classic OLAP.

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How to Build a Performant Data Warehouse in Redshift

Sisense

OLTP vs OLAP. First, we’ll dive into the two types of databases: OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing). An OLAP database is best for situations where you read from the database more often than you write to it. Tech Talk Data Warehouse OLAP

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Infoworks Automated Big Data Engineering

Jen Underwood

Big Data & IoT Data Warehousing Spark Automation Hadoop Sponsored Solution Review OLAPby Jen Underwood. Recently I engaged in a guided “hands-on” evaluation of Infoworks, a “no code” big data engineering solution that expedites and automates Hadoop and cloud workflows.

Data Cube Operations – SQL Queries

Perficient Data & Analytics

An OLAP cube is a data structure that overcomes the limitations of relational databases by providing rapid analysis of data. It is a Multidimensional cube that is built using OLAP databases. Introduction.

Reflecting on the Past Decade in Analytics

Jen Underwood

From what I can remember, in-memory analytics, mobile BI, OLAP cubes and easier to use drag-and-drop analytics tools got all the buzz. by Jen Underwood. What was hot and what was not?

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Why Memory-Centric Architecture Is The Future Of In-Memory Computing

DataFloq

In the earliest days of corporate database architecture, the standard database was an analytical database (OLAP) which imported data from a transactional database (OLTP) after running it through ETL operations every so often to make it palatable to the analytical database.

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The Business Intelligence Market – What’s Old is New

In(tegrate) the Clouds

Thanks to The OLAP Report for lots of great market materials. Comshare, Pilot, Metaphor, watch out here comes some more: OLAP, ROLAP, HOLAP, MOLAP now my head hurts. OLAP for the masses, gents? OLAP Services, TM1, Pablo, Wired, and Crystal fun. Showcase, SQRIBE all get imbibed and don’t forget OLAP@ Work.

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The Enterprise AI Revolution Starts with BI

Jet Global

The optimized data warehouse isn’t simply a number of relational databases cobbled together, however—it’s built on modern data storage structures such as the Online Analytical Processing (or OLAP) cubes. Artificial Intelligence is coming for the enterprise.

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Replacing FRx and Management Reporter: What You Need to Know

Jet Global

Jet Analytics is a business intelligence and reporting solution with pre-built data warehouse and OLAP cubes for Dynamics GP. Data Warehouse and OLAP Cubes.

Best Reporting Tools List You Can’t Miss in 2020

FineReport

The former is more professional in report making, presentation, and printing, while the latter can make OLAP and predict analysis thanks to the BI capabilities. As reporting software, it does not support OLAP.

2019 Highlights in Metadata Management

Octopai

Expanded our support of Microsoft OLAP cube , an innovative open-source feat. As 2019 comes to a close, we think it’s the perfect time to review trends in metadata management as well as look at some of Octopai’s own highlights.

The Future of AI in the Enterprise

Jet Global

The optimized data warehouse isn’t simply a number of relational databases cobbled together, however—it’s built on modern data storage structures such as the Online Analytical Processing (or OLAP) cubes.

Business Intelligence vs. Reporting: Finding Your Bread and Butter

Jet Global

Therefore, the real magic happens when OLAP cubes are built or delivered from the data warehouse. OLAP cubes do all the work by dimensionalizing all combinations of slicing and dicing the data ahead of time.

CCPA 2020: Getting Your Data Landscape Ready

Octopai

As a company’s data landscape grows and evolves, more computing “horsepower” is needed to perform the ETL and OLAP cube processing required to populate data warehouses and drive reports and dashboards.

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What’s the Difference Between Business Intelligence and Business Analytics?

Sisense

BI lets you apply chosen metrics to potentially huge, unstructured datasets, and covers querying, data mining , online analytical processing ( OLAP ), and reporting as well as business performance monitoring, predictive and prescriptive analytics.

The Future of AI in the Enterprise

Jet Global

The optimized data warehouse isn’t simply a number of relational databases cobbled together, however—it’s built on modern data storage structures such as the Online Analytical Processing (or OLAP) cubes.

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Choosing the Reporting Tools: 5 Things You Need To Know

FineReport

‘Business understanding’ means realizing in-depth data analysis and smart data forecast, via BI functions such as OLAP analysis, data mining, and so on. You may have viewed many articles or reviews about reporting tools lists or open-source reporting tools.

Jet vs. Data Entities in Dynamics 365 Finance & Operations

Jet Global

You also get a pre-built data warehouse and cubes (tabular or OLAP) that uses these data entities to de-normalize the tables and keep all your governed data in one place.

Cloud Migrations: Big Challenges, Big Opportunities

Sisense

It’s used to dig up insights for business users, OLAP cubes, analytics apps, and ad-hoc analyses. When your organization decides to pull the trigger on a cloud migration, a lot of stuff will start happening all at once.

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AI vs. BI for Business, What Do You Need?

Jet Global

With all the attention being paid to artificial intelligence (AI) these days, it’s no surprise that enterprise leaders are scrambling to find ways to shoehorn AI implementations into their technology stack.

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Database vs. Data Warehouse: What’s the Difference?

Jet Global

To manage all the integrated data inside a data warehouse, many companies build cubes (OLAP or tabular) for quick reporting and analysis. In the business landscape of 2019, data is the only currency that matters.

Here’s Why Automation For Data Lakes Could Be Important

Smart Data Collective

The way to work around this shortcoming is to use OLAP cubes or data models generated within memory, but these will take time to develop and test, especially since they need to be scalable to the level of use in a data lake. Data Lakes are among the most complex and sophisticated data storage and processing facilities we have available to us today as human beings.

Themes and Conferences per Pacoid, Episode 11

Domino Data Lab

Some may ask: “Can’t we all just go back to the glory days of business intelligence, OLAP, and enterprise data warehouses?” Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. Introduction. Welcome back to our monthly burst of themespotting and conference summaries. BTW, videos for Rev2 are up: [link]. On deck this time ’round the Moon: program synthesis. In other words, using metadata about data science work to generate code.

Self-Serve Data Preparation Doesn’t Mean Traditional ETL is Dead!

Smarten

They can use self-serve data preparation tools to connect to data sources like databases, OLAP cubes and spreadsheets using simple wizard based connection interface.

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SQL Analytics at Scale: Selecting the Right SQL Engine for the Right Job

Cloudera

Since you can mix and match on the same data in CDW on CDP, you can select the right engine for each workload based on workload type, like data engineering, traditional EDW, ad hoc analytics, BI dashboards, Online Analytical Processing (OLAP) or Online Transaction Processing (OLTP).

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