13 Best Databases & Big Data

List Updated June 2020

Bestselling Databases & Big Data in 2020


Big Data: Principles and best practices of scalable realtime data systems

Big Data: Principles and best practices of scalable realtime data systems
BESTSELLER NO. 1 in 2020
  • Manning Publications

Big Data: How the Information Revolution Is Transforming Our Lives (Hot Science)

Big Data: How the Information Revolution Is Transforming Our Lives (Hot Science)
BESTSELLER NO. 2 in 2020

Big Data Fundamentals: Concepts, Drivers & Techniques (The Prentice Hall Service Technology Series from Thomas Erl)

Big Data Fundamentals: Concepts, Drivers & Techniques (The Prentice Hall Service Technology Series from Thomas Erl)
BESTSELLER NO. 3 in 2020

Big Data: Does Size Matter? (Bloomsbury Sigma)

Big Data: Does Size Matter? (Bloomsbury Sigma)
BESTSELLER NO. 4 in 2020

Big Data Made Accessible: 2019 edition

Big Data Made Accessible: 2019 edition
BESTSELLER NO. 5 in 2020

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
BESTSELLER NO. 6 in 2020

Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are

Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are
BESTSELLER NO. 7 in 2020
  • Dey Street Books

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
BESTSELLER NO. 8 in 2020
  • O'Reilly Media

Big Data Appliances for In-Memory Computing: A Real-World Research Guide for Corporations to Tame and Wrangle Their Data

Big Data Appliances for In-Memory Computing: A Real-World Research Guide for Corporations to Tame and Wrangle Their Data
BESTSELLER NO. 9 in 2020

IDE for BIG DATA [Download]

IDE for BIG DATA [Download]
BESTSELLER NO. 10 in 2020
  • NoSQL support
  • Cloud storages support
  • RDBMS
  • Report services
  • Cloud databases and Saas

Hadoop Database EMR Spark Big Data Vinyl Diecut Sticker 4 stickers

Hadoop Database EMR Spark Big Data Vinyl Diecut Sticker 4 stickers
BESTSELLER NO. 11 in 2020
  • 100% Weatherproof,UV ink on Waterproof Vinyl Avery Vinyl
  • Professionally printed using the higest quality using Eco Solvent Ink
  • Long outdoor life and Environmental friendly,temperature resistant
  • Easy to install and remove just peel & stick!
  • Full Color Printed Decal

Big Data: A Primer (Studies in Big Data)

Big Data: A Primer (Studies in Big Data)
BESTSELLER NO. 12 in 2020

Liili Premium Samsung Galaxy S8 Plus Aluminum Backplate Bumper Snap Case ID: 23445524 Big data concept in word tag cloud on black background

Liili Premium Samsung Galaxy S8 Plus Aluminum Backplate Bumper Snap Case ID: 23445524 Big data concept in word tag cloud on black background
BESTSELLER NO. 13 in 2020
  • This item is designed and made for "Samsung Galaxy S8 PLUS" Only
  • Aluminum back plate along with a durable plastic hard shell, completely protect the phone from dirt, scratch and bumps
  • Easy access to all buttons, controls and ports without removing the case. Easy to install - just snap on to your device
  • MADE IN USA. Designed, printed and shipped from our California facility
  • Please search "Liili Samsung Galaxy S8 PLUS" for more special and unique designs

What is the Difference Between OLAP and OLTP?

A brief look at the significant differences between transactional databases and analytical processing databases, which are used most frequently in data warehouses.

PURPOSE
OLTP: Main purpose is to automate operational procedures and provide up-to-the minute information on current business processes. Reporting tends to be workflow related, answering queries such as "which claims are still open?" and "which customers are waiting for a response?"

OLAP:Main purpose of this is for reporting and data analysis. This is where users go to answer queries related to business cause, important trends, and overall profitability. Reports from an OLAP are intended to help with general business decision making and future planning, answering queries such as "which demographic attribute makes customers less likely to submit a claim?" or "which insurance policies are most likely to be purchased by those customers who purchase a boat policy?".

DATA SOURCE
OLTP: Data is input by various clerks and office staff, maintaining an operational log. There can be hundreds of users inputting data simultaneously, or there may be only one user. Regardless of the size, data is in a constant state of change.

OLAP: Data is rolled in from various data sources, most of which are likely OLTPs that are used in several difference business offices. It may also contain data from flat files or data mined from the web or other businesses. Regardless of the sources, data tends to be slowly updated in a scheduled manner, such as nightly or weekly, allowing statistics to be more easily gathered than in the dynamic OLTP.

IMPORTANT METRICS
OLTP: In OLTP, throughput (number of transactions per second) is vitally important, as is minimizing concurrency conflicts. The point is to allow users to insert and update data quickly so that their daily jobs are not disrupted. This means data organization and indexing should be done in a way that increases the insert and update speed, without being overly concerned about the speed of reporting.

OLAP: In OLAP, response time and query throughput are the most important metrics. Since the main purpose is reporting, users expect to be able to gather statistics quickly. Therefore, indexing and data organization may be significantly different from the OLTP, increasing large query response time while not optimizing for inserts and updates.

DATA ORGANIZATION
OLTP:In relational OLTP systems,the data is generally in third-normal form. This lessens the amount of space required and allows fast inserts and updates on data that is logically interrelated. For example, one could update just an event that occurred on a claim, without updating the entire claim itself.

OLAP: In relational OLAP systems, the data will usually be in some sort of denormalized form, often in a star schema format based on "facts" and "dimensions." The flattened schema allows the system to retrieve data by going to fewer tables with clear relationships, which increases the speed of complex queries.

INDEXING
OLTP: Indexing can slow down inserts and updates because both the table and the index have to be updated. Therefore, OLTP systems tend to have only a few indexes per table, representing the most common searches related to workflow reports.

OLAP: When done right,indexing significantly increases the speed of large queries. It is not uncommon for an OLAP table to have an index on every single column.

PHYSICAL ARCHITECTURE
OLTP:Most OLTP databases are in a relational database form, organizing information according to tables that are connected by a common column, or "key".

OLAP:Some OLAP databases may be stored in a relational form, and thus are called ROLAP. Others may be stored in a multi-dimensional engine which stores data in multi-dimensional arrays. These are called MOLAP.

DISK SPACE
OLTP: OLTPs are smaller, generally between a few hundred megabytes and a hundred gigabytes. This is because the third-normal form organization reduces data redundancy and space-intensive indexes are kept to a minimum.

OLAP:OLAPs can be much large, especially since data may come from multiple OLTPs. The denormalized data and extensive indexing also takes up quite a bit of space. OLAPs tend to run in the hundreds of gigabytes into the terabyte range.

In summary, OLAP and OLTP are similar in that they are terms used to describe storage of interrelated data. But because the intended use of the systems are different, the physical and logical architecture need to be different in order to optimize performance.

SOURCES

o/OLTP-vs-OLAP.html

An overview of data warehousing and OLAP technology, by Chaudhuri, Dayal, Microsoft, and Hewlett-Packard, published in ACM SIGMOD, Volume 26, Issue 1, in March 1997.