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Use to Choose Data Analytics

data analytics

By Andrew Thompson

The business environment has rapidly changed over the last one decade. The need for data analysis is now more urgent than ever, especially with emergence of big data. The data management landscape is nothing short of revolutionary and businesses are being forced to choose from a variety of database systems to keep up with the times. Traditional relational database management systems (RDBMS) are already overwhelmed and data analysts and database managers are looking for alternatives in the market.

Impact of Big Data on Analytics

With the increase of data generation volumes and the need for analysis, data analytics has taken a central point in organization models. Data analytics is the science of analyzing raw data collected from business, public and non-governmental organizations. The idea in data analytics is to draw conclusions which can then be used to build future strategies. In science, data analytics is crucial in helping approve theories that have been in place.

Big data has led to a paradigm shift in data analytics with the need for more scalable databases to help reach better conclusions. Businesses today are generating large volumes of data, but this is just one aspect of big data as per the 3 ‘Vs’ model; volume, variety and velocity. While your business might be generating and collecting large volumes of data, what really matters is how this data is utilized to improve performance in your organization.

Data analytics is the science that helps you examine data collected in order to make better decisions regarding your production, marketing and customer interactions. The effectiveness of data analytics for your organization will depend on how efficiently data is stored and this again emphasizes the importance of the database you choose. If your analytics queries are running into the edges of available out-of-the-box tools, you must opt for database-optimized for analytics.

Analytics Database for Your Business

Now that you appreciate the convergence of big data, data analytics and databases, it is importance to start shopping for tools that suit your business. Analytics databases used by remote DBA experts are purposely designed for business intelligence (BI). It is different from an operational or OLTP database, which focuses on transaction processing.

While you can still use an OLTP database to support business intelligence and the data warehouse, an analytic database offers better scalability and higher performance. Before making your choice, it is imperative to examine the common types of analytic databases available including:

  • Data warehouse appliances: Operate on an integrated platform to combine BI tools, the database and hardware being used. These offer an easy-to-use platform for analytical workloads and are quick to install.
  • Columnar databases: They reduce number of data elements to be read by the database by organizing data by columns as opposed to rows. This means faster querying.
  • Online analytical tools: OLAP databases are a popular option today and they allow for data analysis using multiple attributes for comprehensive results.
  • In-memory databases: They attempt to streamline operations in query processing by loading source data in a compressed format into the system memory.
  • MPP databases: Massively parallel processing databases are now a popular alternative to relational database management systems (RDBMS). Data is spread across a cluster of servers and this shared model means more efficiency.

Factors in Choosing an Analytics Database

With these thoughts in mind, you now appreciate the impact big data analytics will have on your organization. It goes without saying that your business must invest in a strong big data analytics database. More importantly, data analytics means more people are now accessing the database and this might lead to problems hence the need for a database optimized for analytics. The first choice will not always be the last as the big data landscape is rapidly changing.

Here are some factors to help you make the right choice:

  1. Type of Data
    There are myriad data formats, some which easily fit into word docs or excel spreadsheets. Amazon Redshift and MySQL are perfect for excel type of data while non-relational (NoSQL) databases are ideal for data that can fit into word docs; think Hadoop and Mongo DB.
  2. Data Volumes
    When handling large volumes of data, a non-relational database is ideal as it doesn’t impose restraints. Hadoop is being adopted rapidly for big data analytics by organizations and even government agencies. If you are handling, say, less than 1 TB data, MySQL would do fine. While there are no restrictions on how much data each of the available databases can handle, other factors will also come to play, especially cost for startups.
  3. Your Personnel Resources
    This is an important consideration as it will determine where the focus of your engineering team will be. Some databases require a lot of time and hands to manage. If you have a small DBA team, for instance, a relational database is perfect, but if you have the resources, you can go for NoSQL. When you hire an experienced DBA, this allows you to use the most sophisticated and effective systems in the market.
  4. Data Query Speeds
    Real-time big data analytics is all the rage but is it necessary for your company? Some issues including fraud detection require real-time monitoring and this call for unstructured databases like Hadoop. On the other hand, you can leverage analytics-optimized databases if your data is not changing rapidly. Redshift, for instance, accommodates large volumes of data and still makes queries fast though not real-time.

There are many other factors to consider but the bottom-line is to buy a product that dovetails into your needs. The database management environment is rapidly changing with new ideas coming and going. It is important to stay grounded to avoid random shifts. More importantly, you should look out for trends such as big analytics in the cloud, big data lakes and predicative analytics, faster and better SQL on Hadoop, and deep learning among others. Ultimately, the choice of a big data analytics database will come down to what your organization is doing with the data being generated.


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