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Determine the Appropriate Techniques for Delivering the Results

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  3. AWS Certified Big Data Specialty
  5. Determine the Appropriate Techniques for Delivering the Results

Tables

  • They list records
  • consist of rows (each row is one record) and columns (each column is a field).
  • Show a lot of information in a structured way
  • May overwhelm users who are just looking for high-level trends.

Charts

  • Easiest way to show the development of one or several data sets.
  • Various types from bar and line charts that show relationship between elements over time
  • Pie charts show the components or proportions between the elements of one whole.
  • Line charts track changes or trends over time and show relationship between two or more variables.
  • Bar charts are used to compare quantities of different categories.
  • Scatter plots show the values of two variables plotted along two axes, the pattern of the resulting points revealing any correlation present between them.

Plots

Distribute two or more data sets over a 2D or even 3D

Show the relationship between these sets and the parameters on the plot. Plots also vary: scatter and bubble plots are the most traditional. Though when it comes to big data, analysts use box plots that enable to visualize the relationship between large volumes of different data.

Maps

Maps are widely-used in different industries. They allow to position elements on relevant objects and areas – geographical maps, building plans, website layouts, etc. Among the most popular map visualizations are heat maps, dot distribution maps, cartograms.

Diagrams and matrices

Diagrams are usually used to demonstrate complex data relationships and links and include various types of data on one visualization. They can be hierarchical, multidimensional, tree-like.

Matrix is a big data visualization technique that allows to reflect the correlations between multiple constantly updating (steaming) data sets.

Amazon QuickSight

  • It is a business analytics service
  • Use to build visualizations
  • perform ad hoc analysis
  • get business insights from data.
  • Can automatically discover AWS data sources
  • Also works with other data sources.
  • Uses a robust in-memory engine (SPICE)
  • Amazon QuickSight offers Standard and Enterprise editions.
  • Choose the language to use in the Amazon QuickSight user interface.
  • The language option is set separately for each individual user.

Amazon QuickSight Components

Data Sources and Data Preparation –

  • use a variety of sources for data analysis, including files, AWS services, and on-premises databases.

Data Preparation

  • It is the process of transforming raw data for use in an analysis.
  • It includes making changes like
    • Filtering out data so you can focus on what’s important to you
    • Renaming fields to make them easier to read
    • Changing data types so they are more useful
    • Adding calculated fields to enhance analysis
    • Creating SQL queries to refine data

SPICE

  • SPICE expands to Super-fast, Parallel, In-memory Calculation Engine.
  • It rapidly performs advanced calculations and serve data.
  • Storage and processing capacity in SPICE speeds up the analytical queries.
  • With SPICE, don’t need to retrieve the data every time you change an analysis or update a visual.

Data Analyses

  • It is the basic workspace
  • Used for creating and interacting with visuals or graphical representations of data.
  • Each analysis contains a collection of visuals that assemble and arrange for the purpose
  • Each analysis can contain stories
  • Stories save a sequential slide show of different iterations of the analysis.
  • It is useful to show changes over time or visual comparisons of data.

Visuals

  • A visual, also called as a data visualization
  • It is a graphical representation of a data set
  • It used a type of
    • Diagram
    • Chart
    • Graph
    • table.
  • All visuals begin in AutoGraph mode, which automatically selects visualization based on the fields selected.

Insights

It is a suggested insight to quickly create a visual.

Suggested insights, officially called ML Insights

propose potentially useful visuals based on a evaluation of your data. You can either choose one from the list, or you can create your own.

Sheets

A sheet is a set of visuals that are viewed together in a single page. When you create an analysis, you place visuals in the workspace on a sheet. You can imagine this a sheet from a newspaper, except that it is filled with data visualizations. You can add more sheets, and make them work separately or together in your analysis.

Stories

A story is a set of one or more scenes (captured visuals) that you can play like a slideshow. You can use these to step through different iterations of an analysis. A scene is a representation of an analysis at a given point in time, or with specific settings. It shows the visuals that are on the analysis at that time, but the data in those visuals continues to update. It is not a static snapshot. You capture a scene for use in a story.

Dashboards

A dashboard is a read-only snapshot of an analysis that you can share with other Amazon QuickSight users for reporting purposes. When you create and publish a dashboard, you specify which users have access to it. They can view and filter the dashboard visuals without changing the underlying data.

QuickSight Working

Begin by choosing a dataset.

You can groom your dataset by adding new data elements, creating calculations, or filtering out data. When you’re satisfied that the dataset works for you, you can start analyzing it through graphics.

First, you can create a visual representation of your data. If you don’t know what kind of chart you should use, Amazon QuickSight can help you by choosing a visual type for you. To see this in action, choose some fields. As you choose more fields, Amazon QuickSight’s AutoGraph changes the type of visual it displays. It adapts to what you choose.

You can continue to add more visuals to the same analysis, based on different views of the same underlying data. To fit more visuals on a page, you can resize and rearrange them. Scroll down to find more space on the page.

By creating a series of visuals, you create a story. This visual narrative tells you what is happening in the subject you’re investigating. You can save multiple stories, using filters to show how the data changes over time. Any of these visual data analyses can be shared with other people, who can then further analyze the data.

You can use Amazon QuickSight to publish data dashboards. These are read-only snapshots that you can share for reporting. If you choose, you can also allow other people to create a new analysis and dashboard based on the one you shared with them.

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