Maria's journey in the data fields

         I took the first sip from my morning coffee when the last summit session had started, somewhere around 15:00. I heavily sank to a sofa and realized that this is the first time I had a chance to sit down today. Another organizer was sitting nearby with the exact "stick a fork in me, I am done" look, staring nowhere. "Oh, I am so tired" - that was my 18-year-old nephew Sonya anchored on the other side of the sofa. She had helped us with registrations and timekeeping in classes and has absorbed all speakers' disappointment with outdated presentation equipment. "Most speakers finish before their time", she said with an exhausted look. "Very smart and intelligent people. I didn't understand anything they talked about." I wish we knew how to make sure the summit meets everyone's expectations. Every sponsor wants to deliver a session. The more sponsored sessions mean fewer slots for community speakers sessions. The toughest part of ...

  Sometimes there are properties in the document with unstructured text, like newspaper articles, blog posts, or book abstracts. The inverted index is easy to build and is similar to the data structures search engines use.  Such document structures can help in various complex search patterns, like common word detection, full-text searches, or document similarity searches, using humming distance or l2distance algorithms. Inverted indexes are useful when the number of keywords is not too large and when the existing data is either totally immutable or rarely changed, but frequently searched. Usually, the documents are "parents," and the words inside the document are "children." To build an inverted index, we invert this relation to make the words "parents" and documents "children": Take all or a subset of keywords from the document and pair it with the document ID DocId1: keyword1 DocId1: keyword2 DocId1: keyword3 DocId2: keyword DocId2: keyword1 Re...

There is a lot of tooling around data enrichment and data orchestration in the Azure cloud and many services with similar features. Azure Data Factory, Azure Databricks, Azure Synapse Pipelines, and SSIS services can move data from one data store to another, clean the data, enrich the data by merging entities, and perform data aggregations. They can transform the data from one format into another and partition it according to the business logic. Why would we choose one service over another? Read my new blog post to find out: https://www.mssqltips.com/sqlservertip/7380/azure-cloud-data-processing-strategy-and-tools-selection/  

Are you using CSV files as a primary file format for your data? CSV is a very useful file format if you want to open the file in Excel and analyze it right away. CSV format stores tabular data in plain text, it is old and was wildly used in the early days of business computing. However, if you plan to keep raw data in the data lake, you should reconsider using CSV. There are many modern file formats that were designed for data analysis. In the cloud world of data lakes and schema-on-read querying systems, like AWS Glue or Databricks, CSV files will slow you down. Today I want to talk about Parquet, a modern file format, invented for fast analytical querying. Parquet files organize data in columns, while CSV files organize data in rows. Columnar storage allows much better compression so Parquet data files need less storage, 1 TB of CSV files can be converted into 100GB of parquet files – which can be a huge money saver when cloud storage is used. This also means that scanning parquet f...

Data management tools are evolving at a great speed, and this can be overwhelming. Data volumes and variety evolve and grow as well. Data Engineers are required to transform those waterfalls of data into business insights. The data is arriving from a vast range of sources, like social-media networks, 3rd party partners or internal micro services. If you are experienced SQL Server DBA, you know how versatile the product is. It is very tempting and feels correct to use the tool that you know the best. We can use SQL Server for almost any data management task. We can use SQL Server to watch over the storage for a new unprocessed files. We can load the raw data into SQL Server Staging Area SQL Server database. We can efficiently clean, enrich and aggregate the data using highly expensive relational database resources (even if you are not using SQL Database in the cloud, every Enterprise edition core still cost about $7K) SQL Server Relational engine high cost echoes the product complex...